11.2.1.3.14 Artistic Recognition, Interpretation

Jiang, S.Q.[Shu-Qiang], Huang, Q.M.[Qing-Ming], Ye, Q.X.[Qi-Xiang], Gao, W.[Wen],
An effective method to detect and categorize digitized traditional Chinese paintings,
PRL(27), No. 7, May 2006, pp. 734-746.
Elsevier DOI 0604
Traditional Chinese painting; Image classification; Edge-size histogram BibRef

Taylor, R.P., Guzman, R., Martin, T.P., Hall, G.D.R., Micolich, A.P., Jonas, D., Scannell, B.C., Fairbanks, M.S., Marlow, C.A.,
Authenticating Pollock paintings using fractal geometry,
PRL(28), No. 6, 15 April 2007, pp. 695-702.
Elsevier DOI 0703
Abstract art; Jackson Pollock; Authenticity; Fractals BibRef

Postma, E.O.[Eric O.], van den Herik, H.J.[H. Jaap], van der Lubbe, J.[Jan],
Paintings and writings in the hands of scientists,
PRL(28), No. 6, 15 April 2007, pp. 671-672.
Elsevier DOI 0703
BibRef

Shen, J.L.[Jia-Lie],
Stochastic modeling western paintings for effective classification,
PR(42), No. 2, February 2009, pp. 293-301.
Elsevier DOI 0810
Classic western paintings; Identification; Image retrieval; Cultural heritage BibRef

Wickramasinghe, W.A.P., Dharmarathne, A.T., Kodikara, N.D.,
A mathematical model for computational aesthetics,
IJCVR(1), No. 3, 2010, pp. 311-324.
DOI Link 1102
BibRef

Lee, J.W.[Joon-Whoan], Park, E.J.[Eun-Jong],
Fuzzy Similarity-Based Emotional Classification of Color Images,
MultMed(13), No. 5, 2011, pp. 1031-1039.
IEEE DOI 1110
evaluating an emotional response to color images. BibRef

Arabadjis, D., Rousopoulos, P., Papaodysseus, C., Exarhos, M., Panagopoulos, M., Papazoglou-Manioudaki, L.,
Optimization in Differentiable Manifolds in Order to Determine the Method of Construction of Prehistoric Wall Paintings,
PAMI(33), No. 11, November 2011, pp. 2229-2244.
IEEE DOI 1110
Fit parametric curves to prehistoric wall painting. BibRef

Libeks, J.[Janis], Turnbull, D.[Douglas],
You Can Judge an Artist by an Album Cover: Using Images for Music Annotation,
MultMedMag(18), No. 4, October-December 2011, pp. 30-37.
IEEE DOI 1112
BibRef

Xu, S.H.[Song-Hua], Jiang, H.[Hao], Lau, F.C.M.[Francis C.M.], Pan, Y.H.[Yun-He],
Computationally Evaluating and Reproducing the Beauty of Chinese Calligraphy,
IEEE_Int_Sys(27), No. 3, May-June 2012, pp. 63-72.
IEEE DOI 1208
BibRef

Hughes, J.M.[James M.], Mao, D.[Dong], Rockmore, D.N.[Daniel N.], Wang, Y.[Yang], Wu, Q.A.[Qi-Ang],
Empirical Mode Decomposition Analysis for Visual Stylometry,
PAMI(34), No. 11, November 2012, pp. 2147-2157.
IEEE DOI 1209
Measurement and comparisons of individual style in the visual arts. BibRef

Rosin, P.[Paul], Collomosse, J.P.[John P.], (Eds.)
Image and Video-based Artistic Stylisation,
Springer2013. ISBN 978-1-4471-4518-9 Buy this book: Image and Video-based Artistic Stylisation (Computational Imaging and Vision)
WWW Link. 1211
Non-photorealistic rendering BibRef

Lin, L., Zeng, K., Wang, Y., Xu, Y.Q., Zhu, S.C.,
Video Stylization: Painterly Rendering and Optimization With Content Extraction,
CirSysVideo(23), No. 4, April 2013, pp. 577-590.
IEEE DOI 1304
BibRef

Xie, N.[Ning], Hachiya, H.[Hirotaka], Sugiyama, M.[Masashi],
Artist Agent: A Reinforcement Learning Approach to Automatic Stroke Generation in Oriental Ink Painting,
IEICE(E96-D), No. 5, May 2013, pp. 1134-1144.
WWW Link. 1305
BibRef

Carneiro, G.,
Artistic Image Analysis Using Graph-Based Learning Approaches,
IP(22), No. 8, 2013, pp. 3168-3178.
IEEE DOI artistic image analysis; Content-based image retrieval 1307
BibRef

Sheng, J.C.[Jia-Chuan], Jiang, J.M.[Jian-Min],
Recognition of Chinese artists via windowed and entropy balanced fusion in classification of their authored ink and wash paintings (IWPs),
PR(47), No. 2, 2014, pp. 612-622.
Elsevier DOI 1311
Analysis of ink wash paintings BibRef

Cooper, B.[Bonnie], Lee, B.B.[Barry B.],
Independence and interaction of luminance and chromatic contributions to spatial hyperacuity performance,
JOSA-A(31), No. 4, April 2014, pp. A394-A400.
DOI Link 1404
Color vision; Psychophysics; Spatial discrimination BibRef

Devinck, F.[Frederic], Gerardin, P.[Peggy], Dojat, M.[Michel], Knoblauch, K.[Kenneth],
Spatial selectivity of the watercolor effect,
JOSA-A(31), No. 4, April 2014, pp. A1-A6.
DOI Link 1404
Vision, color, and visual optics BibRef

Monroy Kuhn, J.A.[Juan Antonio], Bell, P.[Peter], Ommer, B.[Björn],
Morphological analysis for investigating artistic images,
IVC(32), No. 6-7, 2014, pp. 414-423.
Elsevier DOI 1406
BibRef
Earlier:
Shaping Art with Art: Morphological Analysis for Investigating Artistic Reproductions,
VISART12(I: 571-580).
Springer DOI 1210
Shape analysis BibRef

John Asmus: Optical techniques and the mysterious Mona Lisa,
SPIE(Newsroom), April 15, 2014
DOI Link 1407
A video interview. A lifetime in lasers began with one of Maiman's originals at Caltech, and has led this art-restoration pioneer to analyze and restore some of the world's most famous artworks. BibRef

Khan, F.S.[Fahad Shahbaz], Beigpour, S.[Shida], van de Weijer, J.[Joost], Felsberg, M.[Michael],
Painting-91: a large scale database for computational painting categorization,
MVA(25), No. 6, 2014, pp. 1385-1397.
Springer DOI 1408
BibRef

Jacobsen, C.R.[C. Robert], Nielsen, M.[Morten],
Investigation of the Effects of Data Collection on Visual Stylometry,
IJIG(14), No. 04, 2014, pp. 1450019.
DOI Link 1412
analysing visual art by mathematical and statistical methods BibRef

Condorovici, R.G.[Razvan George], Florea, C.[Corneliu], Vertan, C.[Constantin],
Automatically classifying paintings with perceptual inspired descriptors,
JVCIR(26), No. 1, 2015, pp. 222-230.
Elsevier DOI 1502
BibRef
And: Erratum: JVCIR(29), No. 1, 2015, pp. 153-.
Elsevier DOI 1504
Painting BibRef

Abry, P., Roux, S., Wendt, H., Messier, P., Klein, A., Tremblay, N., Borgnat, P., Jaffard, S., Vedel, B., Coddington, J., Daffner, L.,
Multiscale Anisotropic Texture Analysis and Classification of Photographic Prints: Art scholarship meets image processing algorithms,
SPMag(32), No. 4, July 2015, pp. 18-27.
IEEE DOI 1506
Anisotropic magnetoresistance BibRef

Johnson, C., Sethares, W., Ellis, M., Haqqi, S.,
Hunting for Paper Moldmates Among Rembrandt's Prints: Chain-line pattern matching,
SPMag(32), No. 4, July 2015, pp. 28-37.
IEEE DOI 1506
Art BibRef

van Noord, N., Hendriks, E., Postma, E.,
Toward Discovery of the Artist's Style: Learning to recognize artists by their artworks,
SPMag(32), No. 4, July 2015, pp. 46-54.
IEEE DOI 1506
Adaptive filters BibRef

Ghaderi, M.[Mohammad], Ruiz, F.[Francisco], Agell, N.[Núria],
Understanding the impact of brand colour on brand image: A preference disaggregation approach,
PRL(67, Part 1), No. 1, 2015, pp. 11-18.
Elsevier DOI 1511
Preference disaggregation BibRef

Gbèhounou, S.[Syntyche], Lecellier, F.[François], Fernandez-Maloigne, C.[Christine],
Evaluation of local and global descriptors for emotional impact recognition,
JVCIR(38), No. 1, 2016, pp. 276-283.
Elsevier DOI 1605
Images BibRef

Xu, P.F.[Peng-Fei], Zheng, X.[Xia], Chang, X.J.[Xiao-Jun], Miao, Q.G.[Qi-Guang], Tang, Z.Y.[Zhan-Yong], Chen, X.J.[Xiao-Jiang], Fang, D.Y.[Ding-Yi],
Artistic information extraction from Chinese calligraphy works via Shear-Guided filter,
JVCIR(40, Part B), No. 1, 2016, pp. 791-807.
Elsevier DOI 1610
Artistic information extraction BibRef

Sun, T.C.[Tian-Cheng], Wang, Y.L.[Yu-Long], Yang, J.[Jian], Hu, X.L.[Xiao-Lin],
Convolution Neural Networks With Two Pathways for Image Style Recognition,
IP(26), No. 9, September 2017, pp. 4102-4113.
IEEE DOI 1708
feature extraction, image classification, image retrieval, image texture, neural nets, AlexNet CNN, ImageNet classification data set, VGG-19 CNN, artwork analysis, BibRef

Nanni, L.[Loris], Ghidoni, S.[Stefano],
How could a subcellular image, or a painting by Van Gogh, be similar to a great white shark or to a pizza?,
PRL(85), No. 1, 2017, pp. 1-7.
Elsevier DOI 1612
Deep convolutional neural networks BibRef

Puthenputhussery, A.[Ajit], Liu, Q.F.[Qing-Feng], Liu, C.J.[Cheng-Jun],
A Sparse Representation Model Using the Complete Marginal Fisher Analysis Framework and Its Applications to Visual Recognition,
MultMed(19), No. 8, August 2017, pp. 1757-1770.
IEEE DOI 1708
BibRef
Earlier:
Sparse Representation Based Complete Kernel Marginal Fisher Analysis Framework for Computational Art Painting Categorization,
ECCV16(VIII: 612-627).
Springer DOI 1611
Dictionaries, Encoding, Feature extraction, Null space, Optimization, Sparse matrices, Visualization, Discriminative sparse representation, column space, complete marginal Fisher analysis (CMFA), BibRef

Puthenputhussery, A.[Ajit], Liu, Q.F.[Qing-Feng], Liu, H., Liu, C.J.[Cheng-Jun],
Generative and Discriminative Sparse Coding for Image Classification Applications,
WACV18(1824-1832)
IEEE DOI 1806
feature extraction, image classification, image coding, image representation, learning (artificial intelligence), Training BibRef

Chu, W., Wu, Y.,
Image Style Classification Based on Learnt Deep Correlation Features,
MultMed(20), No. 9, September 2018, pp. 2491-2502.
IEEE DOI 1809
art, convolution, correlation methods, feature extraction, feedforward neural nets, image classification, image texture, learnt correlation features BibRef

Jiang, S.H.[Shu-Hui], Shao, M.[Ming], Jia, C.C.[Cheng-Cheng], Fu, Y.[Yun],
Learning Consensus Representation for Weak Style Classification,
PAMI(40), No. 12, December 2018, pp. 2906-2919.
IEEE DOI 1811
Visualization, Feature extraction, Data visualization, Principal component analysis, Encoding, Deep learning, auto-encoder BibRef

Florea, C.[Corneliu], Gieseke, F.[Fabian],
Artistic movement recognition by consensus of boosted SVM based experts,
JVCIR(56), 2018, pp. 220-233.
Elsevier DOI 1811
Randomized boosted SVMs, Multi-scale topography, Painting style recognition, Consensus of experts, Ensembles BibRef

Yang, H.[Heekyung], Min, K.[Kyungha],
Classification of basic artistic media based on a deep convolutional approach,
VC(36), No. 3, March 2020, pp. 559-578.
WWW Link. 2002
BibRef

Garcia, N.[Noa], Renoust, B.[Benjamin], Nakashima, Y.[Yuta],
ContextNet: representation and exploration for painting classification and retrieval in context,
MultInfoRetr(9), No. 1, March 2020, pp. 17-30.
WWW Link. 2003
BibRef

Silva, J.M.[Jorge Miguel], Pratas, D.[Diogo], Antunes, R.[Rui], Matos, S.[Sérgio], Pinho, A.J.[Armando J.],
Automatic analysis of artistic paintings using information-based measures,
PR(114), 2021, pp. 107864.
Elsevier DOI 2103
Image analysis, Data compression, BDM, Artistic paintings, Algorithmic information theory BibRef

Ou, Y.J.[Yang-Jun], Chen, Z.Z.[Zhen-Zhong], Wu, F.[Feng],
Multimodal Local-Global Attention Network for Affective Video Content Analysis,
CirSysVideo(31), No. 5, 2021, pp. 1901-1914.
IEEE DOI 2105
Predicting emotional responses of movie audiences. BibRef

Zhu, Y.C.[Yao-Chen], Chen, Z.Z.[Zhen-Zhong], Wu, F.[Feng],
Affective Video Content Analysis via Multimodal Deep Quality Embedding Network,
AffCom(13), No. 3, July 2022, pp. 1401-1415.
IEEE DOI 2209
Annotations, Feature extraction, Training, Neural networks, Probabilistic logic, Noise measurement, Color, deep neural networks BibRef

Guo, W.Y.[Wen-Ya], Zhang, Y.[Ying], Cai, X.R.[Xiang-Rui], Meng, L.[Lei], Yang, J.F.[Ju-Feng], Yuan, X.J.[Xiao-Jie],
LD-MAN: Layout-Driven Multimodal Attention Network for Online News Sentiment Recognition,
MultMed(23), 2021, pp. 1785-1798.
IEEE DOI 2106
Sentiment analysis, Visualization, Layout, Feature extraction, Analytical models, Neural networks, Image recognition, article layout BibRef

Wu, C.L.[Chun-Lei], Wang, J.N.[Jiang-Nan], Yuan, S.Z.[Shao-Zu], Wang, L.Q.[Lei-Quan], Zhang, W.S.[Wei-Shan],
Generate classical Chinese poems with theme-style from images,
PRL(149), 2021, pp. 75-82.
Elsevier DOI 2108
Image, Quatrain, Automatic generation, Theme, Style BibRef

Pfister, J.[Jan], Kobs, K.[Konstantin], Hotho, A.[Andreas],
Self-Supervised Multi-Task Pretraining Improves Image Aesthetic Assessment,
NTIRE21(816-825)
IEEE DOI 2109
Computational modeling, Neural networks, Training data, Distortion, Data models BibRef

Frank, S.J.[Steven J.],
State of the Art: This Convolutional Neural Network can Tell you Whether a Painting is a Fake,
Spectrum(58), No. 9, September 2021, pp. 26-31.
IEEE DOI 2109
Painting BibRef

Zhu, H.C.[Han-Cheng], Li, L.D.[Lei-Da], Wu, J.J.[Jin-Jian], Zhao, S.C.[Si-Cheng], Ding, G.G.[Gui-Guang], Shi, G.M.[Guang-Ming],
Personalized Image Aesthetics Assessment via Meta-Learning With Bilevel Gradient Optimization,
Cyber(52), No. 3, March 2022, pp. 1798-1811.
IEEE DOI 2203
Task analysis, Adaptation models, Optimization, Visualization, Integrated circuit modeling, Training, small sample learning (SSL) BibRef

Zhu, H.C.[Han-Cheng], Zhou, Y.[Yong], Yao, R.[Rui], Wang, G.C.[Guang-Cheng], Yang, Y.Z.[Yu-Zhe],
Learning image aesthetic subjectivity from attribute-aware relational reasoning network,
PRL(155), 2022, pp. 84-91.
Elsevier DOI 2203
Image aesthetics, Subjectivity, Attribute-aware, Uncertainty, Relational reasoning BibRef

Zhu, H.C.[Han-Cheng], Zhou, Y.[Yong], Li, Q.Y.[Qiao-Yue], Shao, Z.W.[Zhi-Wen],
Personality modeling from image aesthetic attribute-aware graph representation learning,
JVCIR(89), 2022, pp. 103675.
Elsevier DOI 2212
Social media, Personality modeling, Image aesthetics, Attribute-aware graph, Convolutional neural network BibRef

Zhang, Y.Q.[Ya-Qing], Li, X.M.[Xue-Ming], Li, X.W.[Xue-Wei],
Reinforcement learning cropping method based on comprehensive feature and aesthetics assessment,
IET-IPR(16), No. 5, 2022, pp. 1415-1423.
DOI Link 2203
BibRef

Celona, L.[Luigi], Leonardi, M.[Marco], Napoletano, P.[Paolo], Rozza, A.[Alessandro],
Composition and Style Attributes Guided Image Aesthetic Assessment,
IP(31), 2022, pp. 5009-5024.
IEEE DOI 2208
Semantics, Feature extraction, Task analysis, Image color analysis, Convolutional neural networks, Predictive models, Training, hypernetworks BibRef

Wang, Y.Y.[Yuan-Yang], Huang, Y.H.[Yi-Hua], Chen, X.M.[Xiu-Min], Li, L.D.[Lei-Da], Shi, G.M.[Guang-Ming],
Modeling content-attribute preference for personalized image esthetics assessment,
IVC(124), 2022, pp. 104505.
Elsevier DOI 2208
Image esthetics assessment, Esthetic preference, Personality BibRef

Rubio, F.[Fernando], Flores, M.J.[M. Julia], Puerta, J.M.[Jose M.],
Ranking-based scores for the assessment of aesthetic quality in photography,
SP:IC(108), 2022, pp. 116803.
Elsevier DOI 2209
Weakly Supervised Learning, Deep Learning, Transfer Learning, Ranking, Aesthetics scores BibRef

Shu, Y.Y.[Yang-Yang], Li, Q.[Qian], Liu, L.Q.[Ling-Qiao], Xu, G.D.[Guan-Dong],
Privileged multi-task learning for attribute-aware aesthetic assessment,
PR(132), 2022, pp. 108921.
Elsevier DOI 2209
Aesthetic assessment, Privileged information, Multi-task learning BibRef

Shu, Y.Y.[Yang-Yang], Li, Q.[Qian], Liu, L.Q.[Ling-Qiao], Xu, G.D.[Guan-Dong],
Semi-Supervised Adversarial Learning for Attribute-Aware Photo Aesthetic Assessment,
MultMed(26), 2024, pp. 4086-4096.
IEEE DOI 2403
Generators, Task analysis, Training, Adversarial machine learning, Image color analysis, Annotations, Semisupervised learning, photo aesthetic assessment BibRef

Castellano, G.[Giovanna], Vessio, G.[Gennaro],
A Deep Learning Approach to Clustering Visual Arts,
IJCV(130), No. 11, November 2022, pp. 2590-2605.
Springer DOI 2210
BibRef

Jin, X.[Xin], Lou, H.[Hao], Huang, H.[Heng], Li, X.N.[Xin-Ning], Li, X.D.[Xiao-Dong], Cui, S.[Shuai], Zhang, X.K.[Xiao-Kun], Li, X.[Xiqiao],
Pseudo-Labeling and Meta Reweighting Learning for Image Aesthetic Quality Assessment,
ITS(23), No. 12, December 2022, pp. 25226-25235.
IEEE DOI 2212
Training, Feature extraction, Task analysis, Quality assessment, Visualization, Path planning, Autonomous aerial vehicles, path planning BibRef

Zhu, H.C.[Han-Cheng], Zhou, Y.[Yong], Li, L.[Leida], Li, Y.Q.[Ya-Qian], Guo, Y.D.[Yan-Dong],
Learning Personalized Image Aesthetics From Subjective and Objective Attributes,
MultMed(25), 2023, pp. 179-190.
IEEE DOI 2301
Data models, Task analysis, Visualization, Adaptation models, Collaboration, Social networking (online), Image color analysis, aesthetic preferences BibRef

Wang, Y.T.[Yao-Ting], Ke, Y.Z.[Yong-Zhen], Wang, K.[Kai], Guo, J.[Jing], Yang, S.[Shuai],
Spatial-invariant convolutional neural network for photographic composition prediction and automatic correction,
JVCIR(90), 2023, pp. 103751.
Elsevier DOI 2301
Image composition classification, Aesthetic optimization, Space invariance, Deep learning BibRef

Lv, P.[Pei], Fan, J.Q.[Jian-Qi], Nie, X.X.[Xi-Xi], Dong, W.M.[Wei-Ming], Jiang, X.H.[Xiao-Heng], Zhou, B.[Bing], Xu, M.L.[Ming-Liang], Xu, C.S.[Chang-Sheng],
User-Guided Personalized Image Aesthetic Assessment Based on Deep Reinforcement Learning,
MultMed(25), 2023, pp. 736-749.
IEEE DOI 2303
Image enhancement, Task analysis, Reinforcement learning, Feature extraction, Visualization, Training, Neural networks, user interaction BibRef

Niu, Y.Z.[Yu-Zhen], Chen, S.S.[Shan-Shan], Song, B.[Bingrui], Chen, Z.X.[Zhi-Xian], Liu, W.X.[Wen-Xi],
Comment-Guided Semantics-Aware Image Aesthetics Assessment,
CirSysVideo(33), No. 3, March 2023, pp. 1487-1492.
IEEE DOI 2303
Semantics, Multitasking, Task analysis, Fuses, Visualization, Streaming media, Predictive models, multi-task learning BibRef

Ahmad, T.[Tasweer], Schich, M.[Maximilian],
Toward cross-domain object detection in artwork images using improved YoloV5 and XGBoosting,
IET-IPR(17), No. 8, 2023, pp. 2437-2449.
DOI Link 2306
Cross-domain object detection in style-images, clipart, watercolor, and comic images. object detection, neural nets BibRef

Li, L.[Leida], Huang, Y.[Yipo], Wu, J.J.[Jin-Jian], Yang, Y.Z.[Yu-Zhe], Li, Y.Q.[Ya-Qian], Guo, Y.D.[Yan-Dong], Shi, G.M.[Guang-Ming],
Theme-Aware Visual Attribute Reasoning for Image Aesthetics Assessment,
CirSysVideo(33), No. 9, September 2023, pp. 4798-4811.
IEEE DOI 2310
BibRef

Zhang, M.[Mingrui], Li, M.[Mading], Yu, J.H.[Jia-Hao], Chen, L.[Li],
Aesthetic Photo Collage With Deep Reinforcement Learning,
MultMed(25), 2023, pp. 4653-4664.
IEEE DOI 2311
BibRef

Ni, S.[Shijia], Shao, F.[Feng], Chai, X.[Xiongli], Chen, H.W.[Hang-Wei], Ho, Y.S.[Yo-Sung],
Composition-Guided Neural Network for Image Cropping Aesthetic Assessment,
MultMed(25), 2023, pp. 6836-6851.
IEEE DOI 2311
BibRef

Hou, J.W.[Jing-Wen], Lin, W.S.[Wei-Si], Yue, G.H.[Guang-Hui], Liu, W.[Weide], Zhao, B.Q.[Bao-Quan],
Interaction-Matrix Based Personalized Image Aesthetics Assessment,
MultMed(25), 2023, pp. 5263-5278.
IEEE DOI 2311
BibRef

Huang, J.[Jin], Gong, Y.S.[Yong-Shun], Zhang, L.[Lu], Zhang, J.[Jian], Nie, L.Q.[Li-Qiang], Yin, Y.L.[Yi-Long],
Modeling Multiple Aesthetic Views for Series Photo Selection,
MultMed(26), 2024, pp. 1983-1995.
IEEE DOI 2402
Feature extraction, Transformers, Convolutional neural networks, Quality assessment, Visualization, Task analysis, graph neural network BibRef

Zhang, X.D.[Xiao-Dan], Xiao, Y.[Yuan], Peng, J.Y.[Jin-Ye], Gao, X.B.[Xin-Bo], Hu, B.[Bo],
Confidence-based dynamic cross-modal memory network for image aesthetic assessment,
PR(149), 2024, pp. 110227.
Elsevier DOI 2403
Image aesthetic assessment (IAA), Memory-based network, Dynamical multi-modal fusion BibRef

Zhang, K.W.[Kai-Wei], Zhu, D.D.[Dan-Dan], Min, X.K.[Xiong-Kuo], Gao, Z.[Zhongpai], Zhai, G.T.[Guang-Tao],
Synergetic Assessment of Quality and Aesthetic: Approach and Comprehensive Benchmark Dataset,
CirSysVideo(34), No. 4, April 2024, pp. 2536-2549.
IEEE DOI 2404
Databases, Image quality, Distortion, Benchmark testing, Feature extraction, Crowdsourcing, Visualization, channel diversity BibRef

Stork, D.G.[David G.],
Computer Vision, ML, and AI in the Study of Fine Art,
CACM(67), No. 5, May 2024, pp. 68-75.
DOI Link 2405
Ongoing research in the analysis of art is building upon the vast store of algorithms and knowledge from mainstream computer vision, deep learning, and artificial intelligence. BibRef

Yang, S.[Shuai], Wang, Z.[Zibei], Wang, G.[Guangao], Ke, Y.Z.[Yong-Zhen], Qin, F.[Fan], Guo, J.[Jing], Chen, L.M.[Li-Ming],
A self-supervised image aesthetic assessment combining masked image modeling and contrastive learning,
JVCIR(101), 2024, pp. 104184.
Elsevier DOI 2406
Image aesthetic assessment, Self-supervised learning, Masked image modeling BibRef

Wang, Y.[Yong], Song, F.[Fanghao], Liu, Y.[Yan], Li, Y.[Yaying], Wang, W.H.[Wei-Hao], Huang, Q.Q.[Qi-Qi], Hu, Y.[Yang],
Research on the Association Mechanism and Evaluation Model Between fNIRS Data and Aesthetic Quality in Product Aesthetic Quality Evaluation,
AffCom(15), No. 3, July 2024, pp. 1414-1426.
IEEE DOI 2409
Functional near-infrared spectroscopy, Visualization, Product design, Painting, Faces, Mathematical models, functional near-infrared spectroscopy (fNIRS) BibRef

Li, L.[Leida], Zhu, T.[Tong], Chen, P.F.[Peng-Fei], Yang, Y.Z.[Yu-Zhe], Li, Y.Q.[Ya-Qian], Lin, W.S.[Wei-Si],
Image Aesthetics Assessment with Attribute-Assisted Multimodal Memory Network,
CirSysVideo(33), No. 12, December 2023, pp. 7413-7424.
IEEE DOI Code:
WWW Link. 2312
BibRef

Huang, Y.[Yipo], Li, L.[Leida], Chen, P.F.[Peng-Fei], Wu, J.J.[Jin-Jian], Yang, Y.Z.[Yu-Zhe], Li, Y.Q.[Ya-Qian], Shi, G.M.[Guang-Ming],
Coarse-to-Fine Image Aesthetics Assessment With Dynamic Attribute Selection,
MultMed(26), 2024, pp. 9316-9329.
IEEE DOI 2409
Predictive models, Image color analysis, Task analysis, Visualization, Feature extraction, Databases, Merging, model explainability BibRef

Zhu, T.[Tong], Li, L.[Leida], Chen, P.F.[Peng-Fei], Wu, J.J.[Jin-Jian], Yang, Y.Z.[Yu-Zhe], Li, Y.Q.[Ya-Qian],
Emotion-aware hierarchical interaction network for multimodal image aesthetics assessment,
PR(154), 2024, pp. 110584.
Elsevier DOI 2406
Image aesthetics assessment, Multimodal learning, Image emotion analysis BibRef

Yang, Z.C.[Zhi-Chao], Li, L.[Leida], Yang, Y.Z.[Yu-Zhe], Li, Y.Q.[Ya-Qian], Lin, W.S.[Wei-Si],
Multi-Level Transitional Contrast Learning for Personalized Image Aesthetics Assessment,
MultMed(26), 2024, pp. 1944-1956.
IEEE DOI 2402
Task analysis, Feature extraction, Databases, Standards, Videos, Training, aesthetic preferences BibRef

Lan, G.P.[Gui-Peng], Xiao, S.[Shuai], Yang, J.C.[Jia-Chen], Zhou, Y.S.[Yan-Shuang], Wen, J.[Jiabao], Lu, W.[Wen], Gao, X.B.[Xin-Bo],
Image Aesthetics Assessment Based on Hypernetwork of Emotion Fusion,
MultMed(26), 2024, pp. 3640-3650.
IEEE DOI 2402
Task analysis, Visualization, Transformers, Semantics, Feature extraction, Psychology, Videos, image emotion BibRef

Zhang, X.Y.[Xin-Yue], Wang, Z.X.[Zhao-Xia], Cao, G.T.[Gui-Tao], Ho, S.B.[Seng-Beng],
Joint Weakly Supervised Image Emotion Analysis Based on Interclass Discrimination and Intraclass Correlation,
IEEE_Int_Sys(39), No. 5, September 2024, pp. 82-89.
IEEE DOI 2410
Visualization, Adaptation models, Correlation, Machine learning, Feature extraction, Proposals, Intelligent systems, Learning systems BibRef

Cotogni, M.[Marco], Arazzi, M.[Marco], Cusano, C.[Claudio],
PhotoStyle60: A Photographic Style Dataset for Photo Authorship Attribution and Photographic Style Transfer,
MultMed(26), 2024, pp. 10573-10584.
IEEE DOI 2411
Image recognition, Task analysis, Painting, Image color analysis, Feature extraction, Visualization, Digital photography, Dataset, photographic style BibRef

Bleidt, T.[Tibor], Eslami, S.[Sedigheh], de Melo, G.[Gerard],
ArtQuest: Countering Hidden Language Biases in ArtVQA,
WACV24(7311-7320)
IEEE DOI Code:
WWW Link. 2404
Visualization, Art, Pipelines, Benchmark testing, Linguistics, Question answering (information retrieval), Applications, Datasets and evaluations BibRef

Penzel, N.[Niklas], Denzler, J.[Joachim],
Interpreting Art by Leveraging Pre-Trained Models,
MVA23(1-6)
DOI Link 2403
Training, Measurement, Analytical models, Art, Statistical analysis, Computational modeling, Machine vision BibRef

Gao, S.Q.[Si Qi],
A Research on Traditional Tangka Image Classification Based on Visual Features,
CVIDL23(13-16)
IEEE DOI 2403
Tibetian art. Deep learning, Visualization, Image color analysis, Filtering, Support Vector Machine BibRef

Springstein, M.[Matthias], Schneider, S.[Stefanie], Rahnama, J.[Javad], Stalter, J.[Julian], Kristen, M.[Maximilian], Müller-Budack, E.[Eric], Ewerth, R.[Ralph],
Visual Narratives: Large-scale Hierarchical Classification of Art-historical Images,
WACV24(7195-7205)
IEEE DOI 2404
Visualization, Taxonomy, Transformers, Data models, Decoding, Thesauri, Applications, Arts / games / social media, Algorithms, Vision + language and/or other modalities BibRef

He, S.[Shuai], Ming, A.[Anlong], Li, Y.Q.[Ya-Qi], Sun, J.[Jinyuan], Zheng, S.[ShunTian], Ma, H.D.[Hua-Dong],
Thinking Image Color Aesthetics Assessment: Models, Datasets and Benchmarks,
ICCV23(21781-21790)
IEEE DOI 2401
BibRef

Shi, T.F.[Teng-Fei], Chen, C.[Chenglizhao], He, Y.B.[Yuan-Bo], Song, W.F.[Wen-Feng], Hao, A.[Aimin],
Joint Probability Distribution Regression for Image Cropping,
ICIP23(990-994)
IEEE DOI Code:
WWW Link. 2312
BibRef

Dutta, U.K.[Ujjal Kr],
Fuse and Attend: Generalized Embedding Learning for Art and Sketches,
Drawings22(167-183).
Springer DOI 2304
BibRef

Yi, R.[Ran], Tian, H.Y.[Hao-Yuan], Gu, Z.H.[Zhi-Hao], Lai, Y.K.[Yu-Kun], Rosin, P.L.[Paul L.],
Towards Artistic Image Aesthetics Assessment: A Large-scale Dataset and a New Method,
CVPR23(22388-22397)
IEEE DOI 2309
BibRef

Ke, J.J.[Jun-Jie], Ye, K.[Keren], Yu, J.[Jiahui], Wu, Y.H.[Yong-Hui], Milanfar, P.[Peyman], Yang, F.[Feng],
VILA: Learning Image Aesthetics from User Comments with Vision-Language Pretraining,
CVPR23(10041-10051)
IEEE DOI 2309
BibRef

Yang, Y.C.[Yu-Chao], Yang, Y.F.[Yu-Fan], Danzeng, X.[Xire], Zhao, Q.J.[Qi-Jun], Danzeng, P.[Pubu], Li, X.S.[Xin-Sheng], Duoji, G.[Gesang], Gao, D.G.[Ding-Guo],
Learning Multi-Granularity Features for Re-Identifying Figures in Portrait Thangka Images,
ICPR22(1643-1649)
IEEE DOI 2212
Nepal, prayer flags. Representation learning, Visualization, Codes, Convolution, Message passing, Image retrieval, Feature extraction BibRef

Yang, Y.Z.[Yu-Zhe], Xu, L.[Liwu], Li, L.[Leida], Qie, N.[Nan], Li, Y.Q.[Ya-Qian], Zhang, P.[Peng], Guo, Y.D.[Yan-Dong],
Personalized Image Aesthetics Assessment with Rich Attributes,
CVPR22(19829-19837)
IEEE DOI 2210
Photography, Databases, Annotations, Computational modeling, Next generation networking, Datasets and evaluation BibRef

Li, H.T.[Hao-Tang], Guo, S.T.[Sheng-Tao], Lyu, K.L.[Kai-Lin], Yang, X.[Xiao], Chen, T.C.[Tian-Chen], Zhu, J.Q.[Jian-Qing], Zeng, H.Q.[Huan-Qiang],
A Challenging Benchmark of Anime Style Recognition,
VDU22(4720-4729)
IEEE DOI 2210
Protocols, Face recognition, Semantics, Benchmark testing, Metadata, Feature extraction, Transformers BibRef

Xie, X.[Xin], Li, Y.[Yi], Huang, H.B.[Huai-Bo], Fu, H.Y.[Hai-Yan], Wang, W.[Wanwan], Guo, Y.Q.[Yan-Qing],
Artistic Style Discovery with Independent Components,
CVPR22(19838-19847)
IEEE DOI 2210
Photography, Deep learning, Codes, Aerospace electronics, Controllability, Machine learning BibRef

Pistola, T.[Theodora], Georgakopoulou, N.[Nefeli], Shvets, A.[Alexander], Chatzistavros, K.[Konstantinos], Xefteris, V.R.[Vasileios-Rafail], García, A.T.[Alba Táboas], Koulalis, I.[Ilias], Diplaris, S.[Sotiris], Wanner, L.[Leo], Vrochidis, S.[Stefanos], Kompatsiaris, I.[Ioannis],
Imageability-Based Multi-modal Analysis of Urban Environments for Architects and Artists,
FAPER22(198-209).
Springer DOI 2208
BibRef

Withöft, A.[Ani], Abdenebaoui, L.[Larbi], Boll, S.[Susanne],
ILMICA - Interactive Learning Model of Image Collage Assessment: A Transfer Learning Approach for Aesthetic Principles,
MMMod22(II:84-96).
Springer DOI 2203
BibRef

Fiske, L.D., Katsaggelos, A.K., Aalders, M.C.G., Alfeld, M., Walton, M., Cossairt, O.,
A Data Fusion Method for the Delayering of X-Ray Fluorescence Images of Painted Works of Art,
ICIP21(3458-3462)
IEEE DOI 2201
Reflectivity, Art, Image processing, Imaging, Data integration, Fluorescence, XRF, RIS, Hyperspectral imaging, Data Fusion, Cultural Heritage Science BibRef

David, L.O.[Lucas Oliveira], Pedrini, H.[Helio], Dias, Z.[Zanoni], Rocha, A.[Anderson],
Authentication of Vincent van Gogh's Work,
CAIP21(II:371-380).
Springer DOI 2112
BibRef

Holinaty, J.[Josh], Jacobson, A.[Alec], Chevalier, F.[Fanny],
Supporting Reference Imagery for Digital Drawing,
SHE21(2434-2442)
IEEE DOI 2112
Layout, Tools, Distortion, Probes, Interviews BibRef

She, D.Y.[Dong-Yu], Lai, Y.K.[Yu-Kun], Yi, G.X.[Gao-Xiong], Xu, K.[Kun],
Hierarchical Layout-Aware Graph Convolutional Network for Unified Aesthetics Assessment,
CVPR21(8471-8480)
IEEE DOI 2111
Convolutional codes, Image quality, Visualization, Computational modeling, Layout, Neural networks, Psychology BibRef

Agrawal, R.[Rishabh], Sivaprasad, S.[Sarath], Pedanekar, N.[Niranjan],
Color Me Good: Branding in the Coloring Style of Movie Posters,
CVFAD21(3894-3898)
IEEE DOI 2109
Image segmentation, Visualization, Image color analysis, Semantics, Brightness BibRef

Conde, M.V.[Marcos V.], Turgutlu, K.[Kerem],
CLIP-Art: Contrastive Pre-training for Fine-Grained Art Classification,
CVFAD21(3951-3955)
IEEE DOI 2109
Training, Art, Image recognition, Databases, Natural languages, Neural networks BibRef

Santana, P.S.[Priscila Sánchez], Roman-Rangel, E.[Edgar],
Quantifying Visual Similarity for Artistic Styles,
MCPR21(187-197).
Springer DOI 2108
BibRef

Zhang, L.[Lili],
Research on Chinese Traditional Garden Immersive Aesthetic Experience in the Era of Artificial Intelligence,
DHM21(II:415-427).
Springer DOI 2108
BibRef

Lanitis, A., Theodosiou, Z., Partaourides, H.,
Artwork Identification in a Museum Environment: A Quantitative Evaluation of Factors Affecting Identification Accuracy,
EuroMed20(588-595).
Springer DOI 2106
BibRef

Karimi, A.[Akbar], Rossi, L.[Leonardo], Prati, A.[Andrea],
Adversarial Training for Aspect-Based Sentiment Analysis with BERT,
ICPR21(8797-8803)
IEEE DOI 2105
Training, Sentiment analysis, Bit error rate, Neural networks, Task analysis, Software development management BibRef

Thao, H.T.P.[Ha Thi Phuong], Balamurali, B.T., Herremans, D.[Dorien], Roig, G.[Gemma],
AttendAffectNet: Self-Attention based Networks for Predicting Affective Responses from Movies,
ICPR21(8719-8726)
IEEE DOI 2105
Training, Annotations, Neural networks, Streaming media, Predictive models, Motion pictures, Feature extraction BibRef

Fahn, C.S.[Chin-Shyurng], Wu, M.L.[Meng-Luen], Tsau, S.K.[Sheng-Kuei],
An Intelligent Photographing Guidance System Based on Compositional Deep Features and Intepretable Machine Learning Model,
ICPR21(7723-7729)
IEEE DOI 2105
Photography, Computational modeling, Machine learning, Predictive models, Feature extraction, Cameras, preference prediction BibRef

Castellano, G.[Giovanna], Vessio, G.[Gennaro],
Deep Convolutional Embedding for Digitized Painting Clustering,
ICPR21(2708-2715)
IEEE DOI 2105
Measurement, Visualization, Semantics, Metadata, Feature extraction, Knowledge discovery BibRef

Dai, Y.[Ying],
CNN-based repetitive self-revised learning for photos' aesthetics imbalanced classification,
ICPR21(331-338)
IEEE DOI 2105
Training data, photo aesthetic assessment, repetitive self-revised deep learning, dropping out sample, highlight BibRef

Castellano, G.[Giovanna], Vessio, G.[Gennaro],
A Brief Overview of Deep Learning Approaches to Pattern Extraction and Recognition in Paintings and Drawings,
FAPER20(487-501).
Springer DOI 2103
BibRef

Jain, N.[Nitisha], Bartz, C.[Christian], Bredow, T.[Tobias], Metzenthin, E.[Emanuel], Otholt, J.[Jona], Krestel, R.[Ralf],
Semantic Analysis of Cultural Heritage Data: Aligning Paintings and Descriptions in Art-historic Collections,
FAPER20(517-530).
Springer DOI 2103
BibRef

Lin, H.[Hubert], van Zuijlen, M.[Mitchell], Wijntjes, M.W.A.[Maarten W. A.], Pont, S.C.[Sylvia C.], Bala, K.[Kavita],
Insights from a Large-scale Database of Material Depictions in Paintings,
FAPER20(531-545).
Springer DOI 2103
BibRef

Gonthier, N.[Nicolas], Gousseau, Y.[Yann], Ladjal, S.[Saïd],
An Analysis of the Transfer Learning of Convolutional Neural Networks for Artistic Images,
FAPER20(546-561).
Springer DOI 2103
BibRef

Roychowdhury, S.[Shounak],
Color Space Exploration of Paintings Using a Novel Probabilistic Divergence,
FAPER20(577-588).
Springer DOI 2103
BibRef

Aslan, S.[Sinem], Steels, L.[Luc],
Identifying Centres of Interest in Paintings Using Alignment and Edge Detection,
FAPER20(589-603).
Springer DOI 2103
BibRef

Teruggi, S.[Simone], Fassi, F.[Francesco],
Machines Learning for Mixed Reality,
FAPER20(613-627).
Springer DOI 2103
BibRef

Barra, P.[Paola], Barra, S.[Silvio], Narducci, F.[Fabio],
From Fully Supervised to Blind Digital Anastylosis on Dafne Dataset,
FAPER20(628-642).
Springer DOI 2103
BibRef

Olague, G.[Gustavo], Ibarra-Vázquez, G.[Gerardo], Chan-Ley, M.[Mariana], Puente, C.[Cesar], Soubervielle-Montalvo, C.[Carlos], Martinez, A.[Axel],
A Deep Genetic Programming Based Methodology for Art Media Classification Robust to Adversarial Perturbations,
ISVC20(I:68-79).
Springer DOI 2103
BibRef

Sindel, A., Maier, A., Christlein, V.,
Art2Contour: Salient Contour Detection in Artworks Using Generative Adversarial Networks,
ICIP20(788-792)
IEEE DOI 2011
Task analysis, Generative adversarial networks, Painting, Image edge detection, Generators, Training, Artworks BibRef

Cormier, M., Park, S., Beck, L.,
Automatic Classification of Woodcuts and Copperplate Engravings,
CRV20(85-92)
IEEE DOI 2006
Classification, art, illustrations BibRef

Fu, C.P.[Cheng-Peng], Wang, J.Q.[Jin-Qiang], Sang, J.[Jitao], Yu, J.[Jian], Xu, C.S.[Chang-Sheng],
Beyond Literal Visual Modeling: Understanding Image Metaphor Based on Literal-implied Concept Mapping,
MMMod20(I:111-123).
Springer DOI 2003
BibRef

Kim, J., Jun, J.Y., Hong, M., Shim, H., Ahn, J.,
Classification of Oil Painting Using Machine Learning With Visualized Depth Information,
CIPA19(617-623).
DOI Link 1912
BibRef

Wei, H., Gao, G.,
A Holistic Recognition Approach for Woodblock-Print Mongolian Words Based on Convolutional Neural Network,
ICIP19(2726-2730)
IEEE DOI 1910
Convolutional neural network, woodblock-print, classical Mongolian, imbalance distribution of samples, synthetic samples BibRef

Goeting, M.[Marijke],
Seeing the World Through Machinic Eyes: Reflections on Computer Vision in the Arts,
CVAA18(II:653-670).
Springer DOI 1905
BibRef

Lang, S.[Sabine], Ommer, B.[Björn],
Reflecting on How Artworks Are Processed and Analyzed by Computer Vision,
CVAA18(II:647-652).
Springer DOI 1905
BibRef

Sabatelli, M.[Matthia], Kestemont, M.[Mike], Daelemans, W.[Walter], Geurts, P.[Pierre],
Deep Transfer Learning for Art Classification Problems,
CVAA18(II:631-646).
Springer DOI 1905
BibRef

Jangtjik, K.A., Ho, T.T., Yeh, M.C., Hua, K.L.,
A CNN-LSTM framework for authorship classification of paintings,
ICIP17(2866-2870)
IEEE DOI 1803
Adaptation models, Correlation, Logic gates, Painting, Recurrent neural networks, Task analysis, Training, multiscale pyramid representation BibRef

van Noord, N., Postma, E.,
A Learned Representation of Artist-Specific Colourisation,
eHeritage17(2907-2915)
IEEE DOI 1802
Art, Decoding, Image color analysis, Painting, Training, Visualization BibRef

Bianco, S.[Simone], Mazzini, D.[Davide], Schettini, R.[Raimondo],
Deep Multibranch Neural Network for Painting Categorization,
CIAP17(I:414-423).
Springer DOI 1711
BibRef

Florea, C., Toca, C., Gieseke, F.,
Artistic Movement Recognition by Boosted Fusion of Color Structure and Topographic Description,
WACV17(569-577)
IEEE DOI 1609
Art, Databases, Image color analysis, Kernel, Painting, Support vector machines, Training BibRef

Florea, C.[Corneliu], Badea, M.[Mihai], Florea, L.[Laura], Vertan, C.[Constantin],
Domain Transfer for Delving into Deep Networks Capacity to De-Abstract Art,
SCIA17(I: 337-349).
Springer DOI 1706
BibRef

Vitorino, T.[Tatiana], Casini, A.[Andrea], Cucci, C.[Costanza], Picollo, M.[Marcello], Stefani, L.[Lorenzo],
When It Is Not Only About Color: The Importance of Hyperspectral Imaging Applied to the Investigation of Paintings,
CCIW17(175-183).
Springer DOI 1704
BibRef

Bianco, S.[Simone], Cusano, C.[Claudio], Schettini, R.[Raimondo],
Artistic Photo Filtering Recognition Using CNNs,
CCIW17(249-258).
Springer DOI 1704
BibRef

Seguin, B.[Benoit], Striolo, C.[Carlotta], di Lenardo, I.[Isabella], Kaplan, F.[Frederic],
Visual Link Retrieval in a Database of Paintings,
CVAA16(I: 753-767).
Springer DOI 1611
retrieve common visual patterns shared by series of paintings. BibRef

Folego, G., Gomes, O., Rocha, A.,
From impressionism to expressionism: Automatically identifying van Gogh's paintings,
ICIP16(141-145)
IEEE DOI 1610
Art BibRef

Kim, N.[Namil], Choi, Y.[Yukyung], Hwang, S.[Soonmin], Kweon, I.S.[In So],
Artrieval: Painting retrieval without expert knowledge,
ICIP15(1339-1343)
IEEE DOI 1512
color clustering BibRef

Fried, O.[Ohad], Shechtman, E.[Eli], Goldman, D.B.[Dan B.], Finkelstein, A.[Adam],
Finding distractors in images,
CVPR15(1703-1712)
IEEE DOI 1510
regions of an image that draw attention away from the main subjects. Remove them with inpainting. BibRef

Gao, Z.[Zhi], Shan, M.[Mo], Cheong, L.F.[Loong-Fah], Li, Q.Q.[Qing-Quan],
Adaptive Sparse Coding for Painting Style Analysis,
ACCV14(II: 102-117).
Springer DOI 1504
BibRef

Bar, Y.[Yaniv], Levy, N.[Noga], Wolf, L.B.[Lior B.],
Classification of Artistic Styles Using Binarized Features Derived from a Deep Neural Network,
VISART14(71-84).
Springer DOI 1504
BibRef

Chen, Q.[Qian], Carneiro, G.[Gustavo],
Artistic Image Analysis Using the Composition of Human Figures,
VISART14(117-132).
Springer DOI 1504
BibRef

Agarwal, S.[Siddharth], Karnick, H.[Harish], Pant, N.[Nirmal], Patel, U.[Urvesh],
Genre and Style Based Painting Classification,
WACV15(588-594)
IEEE DOI 1503
Accuracy BibRef

Blažek, J.[Jan], Soukup, J.[Jindrich], Zitová, B.[Barbara], Flusser, J.[Jan], Hradilová, J.[Janka], Hradil, D.[David], Tichý, T.[Tomáš],
M3art: A Database of Models of Canvas Paintings,
EuroMed14(176-185).
Springer DOI 1412
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Solina, F.[Franc], Majcen, G.[Gregor], Bovcon, N.[Narvika], Batagelj, B.[Borut],
Preservation of a Computer-Based Art Installation,
EuroMed14(643-650).
Springer DOI 1412
BibRef

Falvo, P.G.[Perla Gianni],
The Madonna of the Goldfinch by Raphael: Chamera of Perception,
EuroMed14(706-715).
Springer DOI 1412
BibRef

Stout, S.[Samantha], Cosentino, A.[Antonino], Scandurra, C.[Carmelo],
Non-invasive Materials Analysis Using Portable X-ray Fluorescence (XRF) in the Examination of Two Mural Paintings in the Catacombs of San Giovanni, Syracuse,
EuroMed14(697-705).
Springer DOI 1412
BibRef

Xiong, B.[Bo], Grauman, K.[Kristen],
Detecting Snap Points in Egocentric Video with a Web Photo Prior,
ECCV14(V: 282-298).
Springer DOI 1408
Those points that look like composed photos. BibRef

Ghorai, M., Chanda, B.,
A robust faint line detection and enhancement algorithm for mural images,
NCVPRIPG13(1-4)
IEEE DOI 1408
art BibRef

Deborah, H.[Hilda], George, S.[Sony], Hardeberg, J.Y.[Jon Yngve],
Pigment Mapping of the Scream (1893) Based on Hyperspectral Imaging,
ICISP14(247-256).
Springer DOI 1406
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Baltazar, A.[Aurélie], Baltazar, P.[Pascal], Frisson, C.[Christian],
An Interactive Device for Exploring Thematically Sorted Artworks,
MMMod14(II: 34-43).
Springer DOI 1405
BibRef

Liu, D.W.[Dong-Wei], Klette, R.[Reinhard],
Inverse Skeletal Strokes,
PSIVTWS13(1-11).
Springer DOI 1402
BibRef

Wu, T.[Tong], Polatkan, G.[Gungor], Steel, D.[David], Brown, W.[William], Daubechies, I.[Ingrid], Calderbank, R.[Robert],
Painting analysis using wavelets and probabilistic topic models,
ICIP13(3264-3268)
IEEE DOI 1402
Hidden Markov Trees BibRef

Guo, X.Y.[Xiao-Ying], Kurita, T.[Takio], Asano, C.M.[Chie Muraki], Asano, A.[Akira],
Visual complexity assessment of painting images,
ICIP13(388-392)
IEEE DOI 1402
Accuracy BibRef

Condorovici, R.G.[Razvan George], Florea, C.[Corneliu], Vertan, C.[Constantin],
Painting Scene Recognition Using Homogenous Shapes,
ACIVS13(262-273).
Springer DOI 1311
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Condorovici, R.G.[Razvan George], Florea, C.[Corneliu], Vrânceanu, R.[Ruxandra], Vertan, C.[Constantin],
Perceptually-Inspired Artistic Genre Identification System in Digitized Painting Collections,
SCIA13(687-696).
Springer DOI 1311
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Abe, K.[Kanako], Saleh, B.[Babak], Elgammal, A.[Ahmed],
An Early Framework for Determining Artistic Influence,
MM4CH13(198-207).
Springer DOI 1309
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Zhang, M.[Min], Atkinson, S.[Sarah], Alechina, N.[Natasha], Qiu, G.P.[Guo-Ping],
'A Is for Art': My Drawings, Your Paintings,
MM4CH13(308-317).
Springer DOI 1309
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Mallik, A.[Anupama], Chaudhury, S.[Santanu], Madan, S.[Shipra], Dinesh, T.B., Chandru, U.V.[Uma V.],
Archiving Mural Paintings Using an Ontology Based Approach,
eHeritage12(II:37-48).
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Tanimoto, T.[Tetsushi], Horiuchi, T.[Takahiko], Tominaga, S.[Shoji],
Precise Estimation of Painting Surfaces for Digital Archiving,
CCIW13(171-183).
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Yamagishi, M.[Misako], Kubo, C.[Chiho], Yamaba, K.[Kazuo],
Considerations of the Affective Factors for Appreciating a Printed-Color Picture,
CCIW13(80-89).
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Okazawa, G.[Gouki], Komatsu, H.[Hidehiko],
Image Statistics for Golden Appearance of a Painting by a Japanese Edo-era Artist Jakuchu Ito,
CCIW13(68-79).
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Wang, Y.[Ying], Takatsuka, M.,
A Framework Towards Quantified Artistic Influences Analysis,
DICTA12(1-8).
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Arora, R.S.[Ravneet Singh], Elgammal, A.[Ahmed],
Towards automated classification of fine-art painting style: A comparative study,
ICPR12(3541-3544).
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Identification of Illustrators,
VISART12(I: 589-597).
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Gancarczyk, J.[Joanna],
Feature Vector Definition for a Decision Tree Based Craquelure Identification in Old Paintings,
VISART12(I: 542-550).
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Carneiro, G.[Gustavo], da Silva, N.P.[Nuno Pinho], del Bue, A.[Alessio], Costeira, J.P.[João Paulo],
Artistic Image Classification: An Analysis on the PRINTART Database,
ECCV12(IV: 143-157).
Springer DOI 1210
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Constable, M., Zhang, X.Y.[Xiao-Yan],
Depth-based Analyses of Landscape Paintings and Photographs According to Itten's Contrasts,
PSIVT10(481-486).
IEEE DOI 1011
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Sun, Y.H.[Yu-Hong], Wang, J.[Jiatao],
Representation of watercolor based on regions,
IASP11(496-500).
IEEE DOI 1112
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Landi, M.[Marco], Maino, G.[Giuseppe],
Multispectral Imaging and Digital Restoration for Paintings Documentation,
CIAP11(II: 464-474).
Springer DOI 1109
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Monti, M.[Mariapaola], Maino, G.[Giuseppe],
Image Processing and a Virtual Restoration Hypothesis for Mosaics and Their Cartoons,
CIAP11(II: 486-495).
Springer DOI 1109
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Barazzetti, L., Scaioni, M., Brumana, R., Remondino, F., Rizzi, A., Lo Brutto, M.,
Geometric and Radiometric Analysis of Paintings,
CloseRange10(xx-yy).
PDF File. 1006
BibRef

Schmidt, N., Schütze, R.[Rainer], Boochs, F.[Frank],
3D-Sutra: Interactive Analysis Tool For A Web-Atlas of Scanned Sutra Inscriptions In China,
CloseRange10(xx-yy).
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Yao, L.[Lei], Li, J.[Jia], Wang, J.Z.[James Z.],
Characterizing elegance of curves computationally for distinguishing Morrisseau paintings and the imitations,
ICIP09(73-76).
IEEE DOI 0911
BibRef

Zhang, Q.[Qing],
Virtual Presenting a Famous Chinese Landscape Painting Based on Textured 3-D Model,
CISP09(1-4).
IEEE DOI 0910
BibRef

Tanaka, N., Tominaga, S.,
Multispectral Analysis of Oil Paintings and Data Compression,
Southwest06(158-162).
IEEE DOI 0603
BibRef

Tominaga, S., Tanaka, N., Komada, T.,
Estimation of surface properties of art paintings using a multi-band camera,
ICPR04(I: 92-95).
IEEE DOI 0409
BibRef
And:
Imaging and rendering of oil paintings using a multi-band camera,
Southwest04(6-10).
IEEE DOI 0411
BibRef

Chapter on 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing continues in
Brush Stroke Analysis .