20.4.2.1.1 Large Scale Systems, Web-Scale System, Learning, Neural Nets

Jing, X.Y.[Xiao-Yuan], Li, S.[Sheng], Zhang, D., Yang, J.[Jian], Yang, J.Y.[Jing-Yu],
Supervised and Unsupervised Parallel Subspace Learning for Large-Scale Image Recognition,
CirSysVideo(22), No. 10, October 2012, pp. 1497-1511.
IEEE DOI 1210
BibRef

Lee, W.Y.[Wen-Yu], Hsieh, L.C.[Liang-Chi], Wu, G.L.[Guan-Long], Hsu, W.[Winston],
Graph-based semi-supervised learning with multi-modality propagation for large-scale image datasets,
JVCIR(24), No. 3, April 2013, pp. 295-302.
Elsevier DOI 1303
Image retrieval; MapReduce; Semi-supervised learning; Large-scale data; Multi-label; Image annotation; Landmark retrieval; Distributed computing BibRef

Negrel, R.[Romain], Picard, D.[David], Gosselin, P.H.[Philippe-Henri],
Web-Scale Image Retrieval Using Compact Tensor Aggregation of Visual Descriptors,
MultMedMag(20), No. 3, 2013, pp. 24-33.
IEEE DOI 1309
Internet BibRef

Negrel, R.[Romain], Picard, D.[David], Gosselin, P.H.[Philippe-Henri],
Dimensionality reduction of visual features using sparse projectors for content-based image retrieval,
ICIP14(2192-2196)
IEEE DOI 1502
BibRef
Earlier:
Efficient Metric Learning Based Dimension Reduction Using Sparse Projectors for Image Near Duplicate Retrieval,
ICPR14(738-743)
IEEE DOI 1412
Approximation methods. Convergence BibRef

Picard, D.[David],
Preserving local spatial information in image similarity using tensor aggregation of local features,
ICIP16(201-205)
IEEE DOI 1610
Convolutional codes BibRef

Gong, Y.C.[Yun-Chao], Lazebnik, S.[Svetlana], Gordo, A.[Albert], Perronnin, F.[Florent],
Iterative Quantization: A Procrustean Approach to Learning Binary Codes for Large-Scale Image Retrieval,
PAMI(35), No. 12, 2013, pp. 2916-2929.
IEEE DOI 1311
BibRef
Earlier: A1, A2, Only:
Iterative quantization: A procrustean approach to learning binary codes,
CVPR11(817-824).
IEEE DOI 1106
Binary codes. Efficient retrieval in large-scale image collections. Find rotation to zero-centered data. BibRef

Zhou, N., Fan, J.,
Jointly Learning Visually Correlated Dictionaries for Large-Scale Visual Recognition Applications,
PAMI(36), No. 4, April 2014, pp. 715-730.
IEEE DOI 1404
Clustering algorithms BibRef

Wang, H.[Han], Wu, X.X.[Xin-Xiao], Jia, Y.D.[Yun-De],
Video Annotation via Image Groups from the Web,
MultMed(16), No. 5, August 2014, pp. 1282-1291.
IEEE DOI 1410
BibRef
Earlier:
Annotating videos from the web images,
ICPR12(2801-2804).
WWW Link. 1302
Transfer info from web image labels to adjacent videos. learning (artificial intelligence). BibRef

Mantziou, E.[Eleni], Papadopoulos, S.[Symeon], Kompatsiaris, Y.[Yiannis],
Learning to detect concepts with Approximate Laplacian Eigenmaps in large-scale and online settings,
MultInfoRetr(4), No. 2, June 2015, pp. 95-111.
Springer DOI 1506
BibRef

Shen, L.[Li], Sun, G.[Gang], Huang, Q.M.[Qing-Ming], Wang, S.H.[Shu-Hui], Lin, Z.C.[Zhou-Chen], Wu, E.[Enhua],
Multi-Level Discriminative Dictionary Learning With Application to Large Scale Image Classification,
IP(24), No. 10, October 2015, pp. 3109-3123.
IEEE DOI 1507
computational complexity BibRef

Tung, F.[Frederick], Little, J.J.[James J.],
Improving scene attribute recognition using web-scale object detectors,
CVIU(138), No. 1, 2015, pp. 86-91.
Elsevier DOI 1506
Affordances. By parts section. BibRef

Martinez, J.[Julieta], Clement, J.[Joris], Hoos, H.H.[Holger H.], Little, J.J.[James J.],
Revisiting Additive Quantization,
ECCV16(II: 137-153).
Springer DOI 1611
BibRef

Tung, F.[Frederick], Little, J.J.[James J.],
Factorized Binary Codes for Large-Scale Nearest Neighbor Search,
BMVC16(xx-yy).
HTML Version. 1805
BibRef
And:
SSP: Supervised Sparse Projections for Large-Scale Retrieval in High Dimensions,
ACCV16(I: 338-352).
Springer DOI 1704
BibRef

Tung, F.[Frederick], Martinez, J.[Julieta], Hoos, H.H.[Holger H.], Little, J.J.[James J.],
Bank of Quantization Models: A Data-Specific Approach to Learning Binary Codes for Large-Scale Retrieval Applications,
WACV15(566-571)
IEEE DOI 1503
Adaptation models BibRef

Ristin, M.[Marko], Guillaumin, M.[Matthieu], Gall, J.[Juergen], Van Gool, L.J.[Luc J.],
Incremental Learning of Random Forests for Large-Scale Image Classification,
PAMI(38), No. 3, March 2016, pp. 490-503.
IEEE DOI 1602
BibRef
Earlier:
Incremental Learning of NCM Forests for Large-Scale Image Classification,
CVPR14(3654-3661)
IEEE DOI 1409
Accuracy. Incremental learning BibRef

Liu, L.[Li], Yu, M.Y.[Meng-Yang], Shao, L.[Ling],
Learning Short Binary Codes for Large-scale Image Retrieval,
IP(26), No. 3, March 2017, pp. 1289-1299.
IEEE DOI 1703
BibRef
Earlier:
Projection Bank: From High-Dimensional Data to Medium-Length Binary Codes,
ICCV15(2821-2829)
IEEE DOI 1602
Binary codes. Lower dimensional feature representation.
See also Kernelized Multiview Projection for Robust Action Recognition. BibRef

Guo, H.Y.[Hai-Yun], Wang, J.Q.[Jin-Qiao], Lu, H.Q.[Han-Qing],
Multiple deep features learning for object retrieval in surveillance videos,
IET-CV(10), No. 4, 2016, pp. 268-271.
DOI Link 1608
BibRef
Earlier:
Learning deep compact descriptor with bagging auto-encoders for object retrieval,
ICIP15(3175-3179)
IEEE DOI 1512
binary codes. Object retrieval; auto-encoder; bagging BibRef

Mai, T.D.[Tien-Dung], Ngo, T.D.[Thanh Duc], Le, D.D.[Duy-Dinh], Duong, D.A.[Duc Anh], Hoang, K.[Kiem], Satoh, S.[Shin'ichi],
Efficient large-scale multi-class image classification by learning balanced trees,
CVIU(156), No. 1, 2017, pp. 151-161.
Elsevier DOI 1702
BibRef
Earlier:
Using node relationships for hierarchical classification,
ICIP16(514-518)
IEEE DOI 1610
BibRef
Earlier:
Learning Balanced Trees for Large Scale Image Classification,
CIAP15(II:3-13).
Springer DOI 1511
BibRef
Earlier: A3, A1, A6, A2, A4, Only:
Efficient Large Scale Image Classification via Prediction Score Decomposition,
ECCV16(VI: 770-785).
Springer DOI 1611
Large-scale image classification BibRef

Xu, X.[Xing], Shen, F.M.[Fu-Min], Yang, Y.[Yang], Shen, H.T.[Heng Tao], Li, X.L.[Xue-Long],
Learning Discriminative Binary Codes for Large-scale Cross-modal Retrieval,
IP(26), No. 5, May 2017, pp. 2494-2507.
IEEE DOI 1704
Binary codes BibRef

Shen, F.M.[Fu-Min], Xu, Y.[Yan], Liu, L.[Li], Yang, Y.[Yang], Huang, Z.[Zi], Shen, H.T.[Heng Tao],
Unsupervised Deep Hashing with Similarity-Adaptive and Discrete Optimization,
PAMI(40), No. 12, December 2018, pp. 3034-3044.
IEEE DOI 1811
Binary codes, Optimization, Image retrieval, Quantization (signal), Adaptation models, Data models, Semantics, Unsupervised learning, image retrieval BibRef

Zhang, L.[Liang], Ma, B.P.[Bing-Peng], Li, G.R.[Guo-Rong], Huang, Q.M.[Qing-Ming], Tian, Q.[Qi],
Cross-Modal Retrieval Using Multiordered Discriminative Structured Subspace Learning,
MultMed(19), No. 6, June 2017, pp. 1220-1233.
IEEE DOI 1705
Correlation, Data models, Feature extraction, Manifolds, Measurement, Multimedia communication, Semantics, Cross-modal retrieval, documents and images, multimedia BibRef

Zhang, L.[Liang], Ma, B.P.[Bing-Peng], Li, G.R.[Guo-Rong], Huang, Q.M.[Qing-Ming], Tian, Q.[Qi],
Generalized Semi-supervised and Structured Subspace Learning for Cross-Modal Retrieval,
MultMed(20), No. 1, January 2018, pp. 128-141.
IEEE DOI 1801
information retrieval, learning (artificial intelligence), GSS-SL, class indicator matrices, cross-modal retrieval, semi-supervised learning BibRef

Wei, P.X.[Peng-Xu], Qin, F.[Fei], Wan, F.[Fang], Zhu, Y.[Yi], Jiao, J.B.[Jian-Bin], Ye, Q.X.[Qi-Xiang],
Correlated Topic Vector for Scene Classification,
IP(26), No. 7, July 2017, pp. 3221-3234.
IEEE DOI 1706
Correlation, Feature extraction, Image coding, Image recognition, Kernel, Semantics, Visualization, Correlated topic vector, Fisher kernel, generative feature learning, semantic correlation. BibRef

Qu, Y., Lin, L., Shen, F., Lu, C., Wu, Y., Xie, Y., Tao, D.,
Joint Hierarchical Category Structure Learning and Large-Scale Image Classification,
IP(26), No. 9, September 2017, pp. 4331-4346.
IEEE DOI 1708
image classification, learning (artificial intelligence), Caltech 256 benchmark dataset, ILSVRC2010 benchmark dataset, hierarchical spectral clustering, joint hierarchical category structure learning, large-scale multiclass classification efficiency improvement, visual tree, Clustering algorithms, Feature extraction, Image representation, Measurement, Prediction algorithms, Semantics, Visualization, Hierarchical learning, N-best path, deep features, large-scale image classification, BibRef

Ma, L., Li, H., Meng, F., Wu, Q., Ngan, K.N.,
Learning Efficient Binary Codes From High-Level Feature Representations for Multilabel Image Retrieval,
MultMed(19), No. 11, November 2017, pp. 2545-2560.
IEEE DOI 1710
Binary codes, Manifolds, Matrix decomposition, Quantization (signal), Semantics, Training, Efficient binary codes, image semantic retrieval, nonnegative, matrix, factorization BibRef

Luo, M.N.[Min-Nan], Chang, X.J.[Xiao-Jun], Li, Z.H.[Zhi-Hui], Nie, L.Q.[Li-Qiang], Hauptmann, A.G.[Alexander G.], Zheng, Q.H.[Qing-Hua],
Simple to complex cross-modal learning to rank,
CVIU(163), No. 1, 2017, pp. 67-77.
Elsevier DOI 1712
Cross-modal retrieval BibRef

Younessian, E.[Ehsan], Mitamura, T.[Teruko], Hauptmann, A.G.[Alexander G.],
Multimodal knowledge-based analysis in multimedia event detection,
ICMR12(51).
DOI Link 1301
Multimedia Event Detection (MED) for retrieval task BibRef

Karpathy, A.[Andrej], Fei-Fei, L.[Li],
Deep Visual-Semantic Alignments for Generating Image Descriptions,
PAMI(39), No. 4, April 2017, pp. 664-676.
IEEE DOI 1703
BibRef
Earlier: CVPR15(3128-3137)
IEEE DOI 1510
Analytical models BibRef

Karpathy, A.[Andrej], Toderici, G.[George], Shetty, S.[Sanketh], Leung, T.[Thomas], Sukthankar, R.[Rahul], Fei-Fei, L.[Li],
Large-Scale Video Classification with Convolutional Neural Networks,
CVPR14(1725-1732)
IEEE DOI 1409
action BibRef

Liang, T.M.[Tian-Ming], Xu, X.Z.[Xin-Zheng], Xiao, P.C.[Peng-Cheng],
A new image classification method based on modified condensed nearest neighbor and convolutional neural networks,
PRL(94), No. 1, 2017, pp. 105-111.
Elsevier DOI 1708
Large-scale, image, classification BibRef

Ercoli, S., Bertini, M., del Bimbo, A.,
Compact Hash Codes for Efficient Visual Descriptors Retrieval in Large Scale Databases,
MultMed(19), No. 11, November 2017, pp. 2521-2532.
IEEE DOI 1710
Indexing, Neural networks, Vector quantization, Visualization, Convolutional neural network (CNN), SIFT, hashing, nearest neighbor search, retrieval BibRef

Wang, J.D.[Jing-Dong], Zhang, T.[Ting], Song, J.K.[Jing-Kuan], Sebe, N.[Nicu], Shen, H.T.[Heng Tao],
A Survey on Learning to Hash,
PAMI(40), No. 4, April 2018, pp. 769-790.
IEEE DOI 1804
learning (artificial intelligence), query processing, data points, evaluation protocols, quantization BibRef

Vo, P.D.[Phong D.], Ginsca, A.L.[Alexandru Lucian], Le Borgne, H.[Hervé], Popescu, A.[Adrian],
Harnessing noisy Web images for deep representation,
CVIU(164), No. 1, 2017, pp. 68-81.
Elsevier DOI 1801
Representation learning BibRef

Ginsca, A.L.[Alexandru Lucian], Popescu, A.[Adrian], Le Borgne, H.[Hervé], Ballas, N.[Nicolas], Vo, P.D.[Phong D.], Kanellos, I.[Ioannis],
Large-Scale Image Mining with Flickr Groups,
MMMod15(I: 318-334).
Springer DOI 1501
BibRef

Hsu, C.C., Lin, C.W.,
CNN-Based Joint Clustering and Representation Learning with Feature Drift Compensation for Large-Scale Image Data,
MultMed(20), No. 2, February 2018, pp. 421-429.
IEEE DOI 1801
Clustering methods, Complexity theory, Feature extraction, Training, Visualization, unsupervised learning BibRef

Li, Y.Q.[Ye-Qing], Liu, W.[Wei], Huang, J.Z.[Jun-Zhou],
Sub-Selective Quantization for Learning Binary Codes in Large-Scale Image Search,
PAMI(40), No. 6, June 2018, pp. 1526-1532.
IEEE DOI 1805
Binary codes, Encoding, Explosives, Image retrieval, Linear matrix inequalities, Principal component analysis, large-scale machine learning BibRef

Fu, Q.[Qiang], Luo, Y.[Yong], Wen, Y.G.[Yong-Gang], Tao, D.C.[Da-Cheng], Li, Y.[Ying], Duan, L.Y.[Ling-Yu],
Toward Intelligent Product Retrieval for TV-to-Online (T2O) Application: A Transfer Metric Learning Approach,
MultMed(20), No. 8, August 2018, pp. 2114-2125.
IEEE DOI 1808
Product on TV, buy it... data handling, Internet, learning (artificial intelligence), pattern classification, pattern matching, query processing, ranking-based loss BibRef

Jiang, H.J.[Hua-Jie], Wang, R.P.[Rui-Ping], Li, Y.[Yan], Liu, H.M.[Hao-Miao], Shan, S.G.[Shi-Guang], Chen, X.L.[Xi-Lin],
Attribute annotation on large-scale image database by active knowledge transfer,
IVC(78), 2018, pp. 1-13.
Elsevier DOI 1809
Attribute, Annotation, Relationship, Active learning, Transfer learning BibRef

Dong, J., Li, X., Xu, D.,
Cross-Media Similarity Evaluation for Web Image Retrieval in the Wild,
MultMed(20), No. 9, September 2018, pp. 2371-2384.
IEEE DOI 1809
image matching, image retrieval, learning (artificial intelligence), query processing, cross-media similarity computation BibRef

Yang, H.F.[Huei-Fang], Lin, K.[Kevin], Chen, C.S.[Chu-Song],
Supervised Learning of Semantics-Preserving Hash via Deep Convolutional Neural Networks,
PAMI(40), No. 2, February 2018, pp. 437-451.
IEEE DOI 1801
constructs binary hash codes from labeled data for large-scale image search. Binary codes, Convolutional codes, Machine learning, Neural networks, Semantics, Synchronous digital hierarchy, supervised hashing BibRef

Peng, Y.X.[Yu-Xin], Qi, J.W.[Jin-Wei], Huang, X.[Xin], Yuan, Y.X.[Yu-Xin],
CCL: Cross-modal Correlation Learning With Multigrained Fusion by Hierarchical Network,
MultMed(20), No. 2, February 2018, pp. 405-420.
IEEE DOI 1801
Birds, Boats, Correlation, Multimedia communication, Optimization, Semantics, Streaming media, Cross-modal retrieval, multi-task learning BibRef

Peng, Y.X.[Yu-Xin], Qi, J.W.[Jin-Wei], Yuan, Y.X.[Yu-Xin],
Modality-Specific Cross-Modal Similarity Measurement With Recurrent Attention Network,
IP(27), No. 11, November 2018, pp. 5585-5599.
IEEE DOI 1809
information retrieval, learning (artificial intelligence), natural language processing, recurrent neural nets, adaptive fusion BibRef

Peng, Y.X.[Yu-Xin], Qi, J.W.[Jin-Wei], Ye, Z.D.[Zhao-Da], Zhuo, Y.K.[Yun-Kan],
Hierarchical Visual-Textual Knowledge Distillation for Life-Long Correlation Learning,
IJCV(129), No. 4, April 2021, pp. 921-941.
Springer DOI 2104
Cross-modal and cross-domain. BibRef

Li, Y.S.[Yan-Sheng], Zhang, Y.J.[Yong-Jun], Huang, X.[Xin], Zhu, H.[Hu], Ma, J.Y.[Jia-Yi],
Large-Scale Remote Sensing Image Retrieval by Deep Hashing Neural Networks,
GeoRS(56), No. 2, February 2018, pp. 950-965.
IEEE DOI 1802
Image retrieval, Learning systems, Machine learning, Manuals, Neural networks, Remote sensing, transfer learning BibRef

Li, Y.S.[Yan-Sheng], Zhang, Y.J.[Yong-Jun], Huang, X.[Xin], Ma, J.Y.[Jia-Yi],
Learning Source-Invariant Deep Hashing Convolutional Neural Networks for Cross-Source Remote Sensing Image Retrieval,
GeoRS(56), No. 11, November 2018, pp. 6521-6536.
IEEE DOI 1811
Remote sensing, Optimization, Image retrieval, Convolutional neural networks, Big Data, Learning systems, source-invariant deep hashing convolutional neural networks (SIDHCNNs) BibRef

Hu, H., Wang, K., Lv, C., Wu, J., Yang, Z.,
Semi-Supervised Metric Learning-Based Anchor Graph Hashing for Large-Scale Image Retrieval,
IP(28), No. 2, February 2019, pp. 739-754.
IEEE DOI 1811
data structures, gradient methods, graph theory, image processing, image retrieval, learning (artificial intelligence), stochastic gradient descent BibRef

Cevikalp, H.[Hakan], Elmas, M.[Merve], Ozkan, S.[Savas],
Large-scale image retrieval using transductive support vector machines,
CVIU(173), 2018, pp. 2-12.
Elsevier DOI 1901
BibRef
Earlier:
Towards Category Based Large-Scale Image Retrieval Using Transductive Support Vector Machines,
WebScale16(I: 621-637).
Springer DOI 1611
Image retrieval, Hashing, Transductive support vector machines, Semi-supervised learning, Ramp loss. binary hierarchical trees. BibRef

Wu, G., Han, J., Guo, Y., Liu, L., Ding, G., Ni, Q., Shao, L.,
Unsupervised Deep Video Hashing via Balanced Code for Large-Scale Video Retrieval,
IP(28), No. 4, April 2019, pp. 1993-2007.
IEEE DOI 1901
binary codes, cryptography, feature extraction, image representation, pattern clustering, unsupervised learning, deep learning BibRef

Xu, J.[Jian], Wang, C.H.[Chun-Heng], Qi, C.Z.[Cheng-Zuo], Shi, C.Z.[Cun-Zhao], Xiao, B.H.[Bai-Hua],
Iterative Manifold Embedding Layer Learned by Incomplete Data for Large-Scale Image Retrieval,
MultMed(21), No. 6, June 2019, pp. 1551-1562.
IEEE DOI 1906
Manifolds, Image retrieval, Learning systems, Computational efficiency, Dimensionality reduction, incomplete data BibRef

Chen, B.H.[Bing-Hui], Deng, W.H.[Wei-Hong],
Deep embedding learning with adaptive large margin N-pair loss for image retrieval and clustering,
PR(93), 2019, pp. 353-364.
Elsevier DOI 1906
Embedding learning, Adaptive margin, Virtual point generating, Discriminative feature BibRef

Keisler, R.[Ryan], Skillman, S.W.[Samuel W.], Gonnabathula, S.[Sunny], Poehnelt, J.[Justin], Rudelis, X.[Xander], Warren, M.S.[Michael S.],
Visual search over billions of aerial and satellite images,
CVIU(187), 2019, pp. 102790.
Elsevier DOI 1909
Visual search, Remote sensing, Machine learning BibRef

Kan, S.C.[Shi-Chao], Cen, L.H.[Li-Hui], Zheng, X.W.[Xin-Wei], Cen, Y.[Yigang], Zhu, Z.[Zhenmin], Wang, H.[Hengyou],
A supervised learning to index model for approximate nearest neighbor image retrieval,
SP:IC(78), 2019, pp. 494-502.
Elsevier DOI 1909
Supervised learning to index, Approximate K-nearest neighbor search, Image relabeling, Codebook learning BibRef

Al-Barazanchi, H.A.[Hussein A.], Qassim, H.[Hussam], Verma, A.[Abhishek],
Large-scale scene image categorisation with deep learning-based model,
IJCVR(10), No. 3, 2020, pp. 185-201.
DOI Link 2005
BibRef

Li, Q.[Qing], Peng, X.J.[Xiao-Jiang], Cao, L.L.[Liang-Liang], Du, W.B.[Wen-Bin], Xing, H.[Hao], Qiao, Y.[Yu], Peng, Q.A.[Qi-Ang],
Product image recognition with guidance learning and noisy supervision,
CVIU(196), 2020, pp. 102963.
Elsevier DOI 2006
BibRef

Chiu, C.Y.[Chih-Yi], Prayoonwong, A.[Amorntip], Liao, Y.C.[Yin-Chih],
Learning to Index for Nearest Neighbor Search,
PAMI(42), No. 8, August 2020, pp. 1942-1956.
IEEE DOI 2007
Indexing, Artificial neural networks, Vector quantization, Hash functions, Binary codes, Approximate nearest neighbor, residual vector quantization BibRef

Prayoonwong, A.[Amorntip], Wang, C.H.[Cheng-Hsien], Chiu, C.Y.[Chih-Yi],
Learning to Index in Large-Scale Datasets,
MMMod18(I:305-316).
Springer DOI 1802
BibRef

Wang, H.[Han], Song, H.[Hao], Wu, X.X.[Xin-Xiao], Jia, Y.D.[Yun-De],
Incremental transfer learning for video annotation via grouped heterogeneous sources,
IET-CV(14), No. 1, February 2020, pp. 26-35.
DOI Link 2002
BibRef
Earlier:
Video Annotation by Incremental Learning from Grouped Heterogeneous Sources,
ACCV14(V: 493-507).
Springer DOI 1504
BibRef

Feng, S.H.[Song-He], Feng, Z.Y.[Zhe-Yun], Jin, R.[Rong],
Learning to Rank Image Tags With Limited Training Examples,
IP(24), No. 4, April 2015, pp. 1223-1234.
IEEE DOI 1503
image classification BibRef

Feng, Z.Y.[Zhe-Yun], Jin, R.[Rong], Jain, A.[Anil],
Large-Scale Image Annotation by Efficient and Robust Kernel Metric Learning,
ICCV13(1609-1616)
IEEE DOI 1403
Efficient BibRef

Chatfield, K.[Ken], Arandjelovic, R.[Relja], Parkhi, O.M.[Omkar M.], Zisserman, A.[Andrew],
On-the-fly learning for visual search of large-scale image and video datasets,
MultInfoRetr(4), No. 2, June 2015, pp. 75-93.
Springer DOI 1506
BibRef

Zisserman, A.[Andrew],
Towards on-the-fly Large Scale Video Search,
BMVC13(xx-yy).
DOI Link 1412
BibRef

Chatfield, K.[Ken], Zisserman, A.[Andrew],
VISOR: Towards On-the-Fly Large-Scale Object Category Retrieval,
ACCV12(II:432-446).
Springer DOI 1304
BibRef

Husain, S.S.[Syed Sameed], Bober, M.[Miroslaw],
REMAP: Multi-Layer Entropy-Guided Pooling of Dense CNN Features for Image Retrieval,
IP(28), No. 10, October 2019, pp. 5201-5213.
IEEE DOI 1909
Feature extraction, Training, Entropy, Image retrieval, Visualization, Aggregates, KL-divergence BibRef

Zhu, H.P.[Hong-Peng],
Massive-scale image retrieval based on deep visual feature representation,
JVCIR(70), 2020, pp. 102738.
Elsevier DOI 2007
Image retrieval, Visual feature, DNN BibRef

Li, S.Y.[Si-Yang], Guo, Y.[Yu], Ren, H.[Hao], Wang, Z.[Ziyi], Ren, K.Y.[Ke-Yan], Liu, C.S.[Chun-Sheng], Lin, H.[Hua], Shi, J.B.[Jian-Bo],
FCNet: A feature context network based on ensemble framework for image retrieval,
IET-CV(16), No. 4, 2022, pp. 295-306.
DOI Link 2205
convolution, image retrieval, learning (artificial intelligence) BibRef

Janani, T., Brindha, M.,
SEcure Similar Image Matching (SESIM): An Improved Privacy Preserving Image Retrieval Protocol over Encrypted Cloud Database,
MultMed(24), 2022, pp. 3794-3806.
IEEE DOI 2208
Image retrieval, Cloud computing, Feature extraction, Protocols, Privacy, Image matching, Encryption, Image matching, feature vectors, similarity matching and cloud BibRef

Chen, B.[Bin], Feng, Y.[Yan], Dai, T.[Tao], Bai, J.[Jiawang], Jiang, Y.[Yong], Xia, S.T.[Shu-Tao], Wang, X.[Xuan],
Adversarial Examples Generation for Deep Product Quantization Networks on Image Retrieval,
PAMI(45), No. 2, February 2023, pp. 1388-1404.
IEEE DOI 2301

WWW Link. Quantization (signal), Image retrieval, Perturbation methods, Feature extraction, Task analysis, Databases, KL divergence BibRef

Xu, H.H.[Hong-Hao], Lai, Z.H.[Zhi-Hui], Kong, H.[Heng],
Jointly sparse fast hashing with orthogonal learning for large-scale image retrieval,
SP:IC(119), 2023, pp. 117062.
Elsevier DOI 2310
Hash learning, Regression model, Feature extraction, Image retrieval BibRef

Song, C.H.[Chull Hwan], Han, H.J.[Hye Joo], Avrithis, Y.S.[Yannis S.],
All the attention you need: Global-local, spatial-channel attention for image retrieval,
WACV22(439-448)
IEEE DOI 2202
Training, Representation learning, Tensors, Image retrieval, Pipelines, Focusing, Network architecture, Image/Video Indexing and Retrieval Deep Learning BibRef

Avrithis, Y.S.[Yannis S.], Kalantidis, Y.[Yannis], Anagnostopoulos, E., Emiris, I.Z.,
Web-Scale Image Clustering Revisited,
ICCV15(1502-1510)
IEEE DOI 1602
Artificial neural networks BibRef

Li, H.K.[Hong-Kai], Bai, C.[Cong], Huang, L.[Ling], Jiang, Y.G.[Yu-Gang], Chen, S.Y.[Sheng-Yong],
Instance Image Retrieval with Generative Adversarial Training,
MMMod20(I:381-392).
Springer DOI 2003
retrieve similar images BibRef

Zhen, L.L.[Liang-Li], Hu, P.[Peng], Wang, X.[Xu], Peng, D.Z.[De-Zhong],
Deep Supervised Cross-Modal Retrieval,
CVPR19(10386-10395).
IEEE DOI 2002
BibRef

Morozov, S.[Stanislav], Babenko, A.[Artem],
Unsupervised Neural Quantization for Compressed-Domain Similarity Search,
ICCV19(3036-3045)
IEEE DOI 2004
data compression, image coding, image retrieval, neural net architecture, quantisation (signal), table lookup, Databases BibRef

Yu, T.[Tan], Yuan, J.S.[Jun-Song], Fang, C.[Chen], Jin, H.L.[Hai-Lin],
Product Quantization Network for Fast Image Retrieval,
ECCV18(I: 191-206).
Springer DOI 1810
BibRef

Jose, A., Lopez, R.D., Heisterklaus, I., Wien, M.,
Pyramid Pooling of Convolutional Feature Maps for Image Retrieval,
ICIP18(480-484)
IEEE DOI 1809
Feature extraction, Convolutional codes, Image retrieval, Neural networks, Poles and towers, Spatial resolution, deep learning BibRef

Jose, A., Yan, S., Heisterklaus, I.,
Binary hashing using siamese neural networks,
ICIP17(2916-2920)
IEEE DOI 1803
Binary codes, Convolutional codes, Feature extraction, Feeds, Neural networks, Training, Training data, Binary hashing, Similar image pairs BibRef

Veit, A., Alldrin, N., Chechik, G., Krasin, I., Gupta, A., Belongie, S.J.[Serge J.],
Learning from Noisy Large-Scale Datasets with Minimal Supervision,
CVPR17(6575-6583)
IEEE DOI 1711
Cleaning, Neural networks, Noise measurement, Robustness, Visualization BibRef

Yuan, T.T.[Tong-Tong], Deng, W.H.[Wei-Hong], Hu, J.[Jiani],
Supervised hashing with extreme learning machine,
VCIP17(1-4)
IEEE DOI 1804
binary codes, computational complexity, cryptography, file organisation, image coding, image retrieval, Target code learning BibRef

Zhou, Y.F.[Yue-Fu], Huang, S.S.[Shan-Shan], Zhang, Y.[Ya], Wang, Y.F.[Yan-Feng],
Deep hashing with triplet quantization loss,
VCIP17(1-4)
IEEE DOI 1804
binary codes, file organisation, image representation, image retrieval, learning (artificial intelligence), supervised hashing BibRef

Zhu, H.[Hao], Wang, F.[Feng], Xiang, X.[Xiang], Tran, T.D.[Trac D.],
Supervised hashing with jointly learning embedding and quantization,
ICIP17(3715-3719)
IEEE DOI 1803
Binary codes, Image color analysis, Linear programming, Optimization, Quantization (signal), Semantics, Training, Supervised Hashing BibRef

Raziperchikolaei, R., Carreira-Perpiñán, M.Á.,
Learning supervised binary hashing: Optimization vs diversity,
ICIP17(3695-3699)
IEEE DOI 1803
Approximation algorithms, Binary codes, Laplace equations, Linear programming, Minimization, Optimization, Training, optimization BibRef

Da, C., Yang, Y., Ding, K., Huo, C., Xiang, S., Pan, C.,
Efficient similarity learning for asymmetric hashing,
ICIP17(865-869)
IEEE DOI 1803
Binary codes, Closed-form solutions, Databases, Dimensionality reduction, Measurement, Semantics, Training, bilinear similarity measure BibRef

Kazi, A.[Anees], Conjeti, S.[Sailesh], Katouzian, A.[Amin], Navab, N.[Nassir],
Coupled Manifold Learning for Retrieval Across Modalities,
Manifold17(1321-1328)
IEEE DOI 1802
BibRef

Noh, H., Araujo, A., Sim, J., Weyand, T., Han, B.,
Large-Scale Image Retrieval with Attentive Deep Local Features,
ICCV17(3476-3485)
IEEE DOI 1802
feature extraction, image matching, image retrieval, learning (artificial intelligence), neural nets, Visualization BibRef

Ahmed, K.[Karim], Torresani, L.[Lorenzo],
BranchConnect: Image Categorization with Learned Branch Connections,
WACV18(1244-1253)
IEEE DOI 1806
feature extraction, feedforward neural nets, image classification, learning (artificial intelligence), Training BibRef

Ahmed, K.[Karim], Baig, M.H.[Mohammad Haris], Torresani, L.[Lorenzo],
Network of Experts for Large-Scale Image Categorization,
ECCV16(VII: 516-532).
Springer DOI 1611
BibRef

Fu, J.L.[Jian-Long], Wu, Y.[Yue], Mei, T.[Tao], Wang, J.Q.[Jin-Qiao], Lu, H.Q.[Han-Qing], Rui, Y.[Yong],
Relaxing from Vocabulary: Robust Weakly-Supervised Deep Learning for Vocabulary-Free Image Tagging,
ICCV15(1985-1993)
IEEE DOI 1602
Machine learning. Tagging web images. BibRef

Xia, Y.[Yan], Cao, X.D.[Xu-Dong], Wen, F.[Fang], Hua, G.[Gang], Sun, J.[Jian],
Learning Discriminative Reconstructions for Unsupervised Outlier Removal,
ICCV15(1511-1519)
IEEE DOI 1602
Outlier images from collection. BibRef

Luo, J.W.[Jian-Wei], Jiang, Z.G.[Zhi-Guo],
Learning Semantic Binary Codes by Encoding Attributes for Image Retrieval,
ICPR14(279-284)
IEEE DOI 1412
Binary codes BibRef

Ushiku, Y.[Yoshitaka], Hidaka, M.[Masatoshi], Harada, T.[Tatsuya],
Three Guidelines of Online Learning for Large-Scale Visual Recognition,
CVPR14(3574-3581)
IEEE DOI 1409
BibRef

Movshovitz-Attias, Y.[Yair], Kanade, T.[Takeo], Sheikh, Y.[Yaser],
How Useful Is Photo-Realistic Rendering for Visual Learning?,
DeepLearn16(III: 202-217).
Springer DOI 1611
BibRef

Chen, X.[Xinlei], Shrivastava, A.[Abhinav], Gupta, A.[Abhinav],
Enriching Visual Knowledge Bases via Object Discovery and Segmentation,
CVPR14(2035-2042)
IEEE DOI 1409
BibRef
Earlier:
NEIL: Extracting Visual Knowledge from Web Data,
ICCV13(1409-1416)
IEEE DOI 1403
Never Ending Image Learner. Runs on web, collecting visual features from web images.
See also Building Part-Based Object Detectors via 3D Geometry. BibRef

Xia, T.[Tian], Tang, Y.Y., Wei, Y.[Yantao], Li, H.[Hong], Li, L.Q.[Luo-Qing],
Object categorization based on hierarchical learning,
ICPR12(1419-1422).
WWW Link. 1302
local coding, maximum pooling. BibRef

Zhang, L.[Lei], Ma, J.[Jun], Cui, C.R.[Chao-Ran], Li, P.[Piji],
Active learning through notes data in Flickr: an effortless training data acquisition approach for object localization,
ICMR11(46).
DOI Link 1301
BibRef

Cao, S.[Song], Snavely, N.[Noah],
Learning to Match Images in Large-Scale Collections,
WebScale12(I: 259-270).
Springer DOI 1210
BibRef

Collins, B.[Brendan], Deng, J.[Jia], Li, K.[Kai], Fei-Fei, L.[Li],
Towards Scalable Dataset Construction: An Active Learning Approach,
ECCV08(I: 86-98).
Springer DOI 0810
Separate relevant images from noise (e.g. internet search) BibRef

Chapter on Implementations and Applications, Databases, QBIC, Video Analysis, Hardware and Software, Inspection continues in
Social Media Search, Large Scale Systems, Web-Scale System .