7.9 Review, Comparison, Evaluation of Texture Analysis Techniques

CUReT: Columbia-Utrecht Reflectance and Texture Database,
2006. Dataset, Texture.
WWW Link.

MIT Texture Data,
1995. Dataset, Texture.
HTML Version.

Texture Data,
2006. Dataset, Texture.
WWW Link.

Outex: New framework for empirical evaluation of texture analysis algorithms,
2006. Dataset, Texture.
WWW Link.

Texure Image Data,
2006. Dataset, Texture.
WWW Link. A variety of texture datasets. Includes Brodatz.

The KTH-TIPS and KTH-TIPS2 image databases,
2006. Dataset, Texture.
WWW Link. Textures under varying illumination, pose and scale. Extension of:
See also CUReT: Columbia-Utrecht Reflectance and Texture Database.

TILDA: Textile Texture Database,
1996. Dataset, Texture.
WWW Link.

Describable Textures Dataset (DTD),
2014 Dataset, Texture.
WWW Link.
See also Describing Textures in the Wild.

Ahuja, N., Schacter, B.,
Image Models,
Surveys(13), No. 4, December 1981, pp. 373-397. BibRef 8112
And: Comments: Surveys(15), No. 1, March 1983, pp. 83-84. Survey, Texture. Mostly about texture representation. BibRef

Ahuja, N., Schacter, B.,
Pattern Models,
WileyNew York, 1983. BibRef 8300

Schacter, B.,
Real Time Display of Textures,
ICPR80(789-791). BibRef 8000

Wechsler, H.,
Texture Analysis: A Survey,
SP(2), 1980, 271-282. Survey, Texture. BibRef 8000

O'Toole, R.K., Stark, H.,
Comparative Study of Optical-Digital vs. All-Digital Techniques in Textural Pattern Recognition,
AppOpt(19), 1980, 2496-2506. BibRef 8000

Van Gool, L.J., Dewaele, P., Oosterlinck, A.,
Texture Analysis Anno 1983,
CVGIP(29), No. 3, 1985, pp. 336-357.
Elsevier DOI Survey, Texture. Texture, Survey. A recent review of texture analysis methods. Statistical and structural methods. Gray level difference method, filter mask texture measures, Fourier power spectrum analysis, cooccurrence features, gray level run lengths, autocorrelation features, methods derived from texture models, relative extrema measures, and gray level profiles. BibRef 8500

Haralick, R.M.,
Statistical Image Texture Analysis,
HPRIP86(247-279). BibRef 8600

Tuceryan, M., Jain, A.K.,
Texture Analysis,
HPRCV92(II-1), 1993, pp. 235-276. Texture review. BibRef 9300

Rao, A.R.,
A Taxonomy for Texture Description and Identification,
Springer-Verlag:Berlin, 1990. BibRef 9000 Ph.D.Thesis (EE), UMich. The thesis as a BibRef BookCreates a measure to distinguish a large number of textures. BibRef

Tomita, F., Tsuji, S.,
Computer Analysis of Visual Textures,
Hingham, MA: KluwerAcademic, August 1990. ISBN 0-7923-9114-4. Survey of theories and techniques for texture analysis.
WWW Link. Survey, Texture. BibRef 9008

Haralick, R.M.,
Statistical and Structural Approaches to Texture,
PIEEE(67), No. 5, May 1979, pp. 786-804. BibRef 7905
Earlier: ICPR78(45-69). Survey, Texture. Texture, Survey. A good review of texture. BibRef

Julesz, B.,
Foundations of Cyclopean Perception,
The University of Chicago Press1971. BibRef 7100

Julesz, B.,
Visual Pattern Discrimination,
IT(8), No. 2, February 1962, pp. 84-92. BibRef 6202

Julesz, B.,
Experiments in the Visual Perception of texture,
SciAmer(232), No. 4, April 1975, pp. 34-43. BibRef 7504

Caelli, T.M., Julesz, B.,
On Perceptual Analyzers Underlying Visual Texture Discrimination: Part I,
BioCyber(28), 1978, pp. 167-176. BibRef 7800

Caelli, T.M., Julesz, B., Gilbert, E.N.,
On Perceptual Analyzers Underlying Visual Texture Discrimination: Part II,
BioCyber(29), No. 4, 1978, pp. 201-214. BibRef 7800

Julesz, B., Caelli, T.M.,
On the Limits of Fourier Decompositions in Visual Texture Perception,
Perception(8), 1978, pp. 69-73. BibRef 7800

Julesz, B., Gilbert, E.N., Victor, J.D.,
Visual Discrimination of Textures with Identical Third-Order Statistics,
BioCyber(31), No. 3, 1979, pp. 137-140. BibRef 7900

Julesz, B., Bergen, R.,
Textons, The Fundamental Elements in Preattentive Vision and Perception of Textures,
Bell System Tech.(62), No. 6, 1983, Part II, pp. 1619-1645. Reprinted in BibRef 8300 RCV87(243-256). BibRef

Julesz, B., Bergen, R.,
Textons, The Elements of Texture Perception, and Their Interactions,
Nature(290), 1981, pp. 91-97. BibRef 8100

Julesz, B.,
Spatial Nonlinearities in the Instantaneous Perception of Textures with Identical Power Spectra,
Royal(B-290), 1980, pp. 83-94. BibRef 8000

Julesz, B.,
A Theory of Preattentive Texture Discrimination Based on First-Order Statistics of Textons,
BioCyber(41), 1981, pp. 131-181. BibRef 8100

Kashi, R.S., Papathomas, T.V., Gorea, A., Julesz, B.,
Similarities Between Texture Grouping and Motion Perception: The Role of Color, Luminance, and Orientation,
IJIST(7), No. 2, Summer 1996, pp. 85-91. 9607
BibRef

Brodatz, P.,
Textures,
New York: Dover1966. BibRef 6600 BookThe standard reference for where to find natural textures. BibRef

Longuet-Higgins, M.S.,
The Statistical Analysis of a Random Moving Surface,
Royal(A-249), February 1957, pp. 321-387.
See also Multiple Interpretations of a Pair of Images of a Surface. BibRef 5702

Longuet-Higgins, M.S.,
Statistical Properties of an Isotropic Random Surface,
Royal(A-250), October 1957, pp. 151-171. BibRef 5710

Koenderink, J.J., van Doorn, A.J.,
Illuminance Texture Due to Surface Mesostructure,
JOSA-A(13), No. 3, March 1996, pp. 452-463. BibRef 9603

Stavridi, M.[Marigo], Koenderink, J.J.,
Studies of 3D Model Textures,
ICIP96(III: 157-160).
IEEE DOI 9610
BibRef

Waksman, A.[Adlai], Rosenfeld, A.[Azriel],
Sparse, Opaque Three-Dimensional Texture. I. Arborescent Patterns,
CVGIP(57), No. 3, May 1993, pp. 388-399.
DOI Link BibRef 9305

Waksman, A.[Adlai], Rosenfeld, A.[Azriel],
Sparse, Opaque 3-Dimensional Texture, 2B: Photometry,
PR(29), No. 2, February 1996, pp. 297-313.
Elsevier DOI BibRef 9602
And: Erratum: PR(29), No. 6, June 1996, pp. R1-R2.
Elsevier DOI 9606
BibRef UMDTR3363, 1994.
WWW Link. BibRef

Waksman, A.[Adlai], Rosenfeld, A.[Azriel],
Sparse, Opaque 3-Dimensional Texture, 2A: Visibility,
GMIP(58), No. 2, March 1996, pp. 155-163. BibRef 9603
And: UMDTR3333, 1994.
WWW Link. And
WWW Link. BibRef

Waksman, A.[Adlai], Rosenfeld, A.[Azriel],
Assessing the Condition of a Plant,
MVA(10), No. 1, 1997, pp. 35-41.
Springer DOI 9705
BibRef

Ohanian, P.P.[Philippe P.], Dubes, R.C.[Richard C.],
Performance Evaluation for Four Classes of Textural Features,
PR(25), No. 8, August 1992, pp. 819-833.
Elsevier DOI BibRef 9208

Zhu, Y.M., Goutte, R.,
A Comparison of Bilinear Space Spatial-Frequency Representations for Texture-Discrimination,
PRL(16), No. 10, October 1995, pp. 1057-1068. BibRef 9510

Smith, G., Burns, I.,
Measuring Texture Classification Algorithms,
PRL(18), No. 14, December 1997, pp. 1495-1501. 9806
MEASTEX. Compared various methods. Implemented a number of them. At least:M MRF (
See also Classification of Textures Using Gaussian Markov Random Fields. ), GLCM (
See also Theoretical Comparison of Texture Algorithms, A. ), Fractal Dimension (
See also Improved Fractal Geometry Based Texture Segmentation Technique. ), Gabor Convolution Energies (
See also Gabor Filters as Texture Discriminator. ). BibRef

Smith, G.[Guy], Burns, I.[Ian],
Benchmarking Texture Classification Algorithms,
TR1997.
HTML Version. Strictly speaking only an online "paper," with no printed reference at this time. A means to evaluate texture algorithms with a database, results of comparing several well-known algorithms, implementations, descriptions, programs, etc. Algorithms categories include: Grey Level Cooccurrence Matrices (
See also Textural Features for Image Classification.
See also Theoretical Comparison of Texture Algorithms, A. ), Gabor Energies (
See also Gabor Filters as Texture Discriminator. ), and Gauss Markov Random Fields (
See also Classification of Textures Using Gaussian Markov Random Fields. ). BibRef 9700

Randen, T.[Trygve], Husøy, J.H.[John Håkon],
Filtering for Texture Classification: A Comparative Study,
PAMI(21), No. 4, April 1999, pp. 291-310.
IEEE DOI Texture, Evaluation. Reviews the major filter approaches, noting the past problems and conflicting results for some evaluations. Laws filters (
See also Textured Image Segmentation. ), Ring/Wedge filters, Dyadic (wavelet) Gabor Decompositions(
See also Texture Segmentation Using 2-D Gabor Elementary Functions. ), DCT, Co-Occurrence (
See also Textural Features for Image Classification. ), Autoregressive, Daubechies wavelets, Eigenfilter, etc. No one approach did best, some did better on some images, worse on others. An important comment regards separation of test and training data, do not trust results that test on training data.
See also Texture Segmentation with Optimal Linear Prediction Error Filters. BibRef 9904

Al-Janobi, A.[Abdulrahman],
Performance evaluation of cross-diagonal texture matrix method of texture analysis,
PR(34), No. 1, January 2001, pp. 171-180.
Elsevier DOI 0010
BibRef

Sipilä, O.[Outi], Visa, A., Salonen, O., Erkinjuntti, T., Katila, T.,
Experiences on data quality in automatic tissue classification,
PRL(22), No. 14, December 2001, pp. 1475-1482.
Elsevier DOI 0110
BibRef

Zhang, J.G.[Jian-Guo], Tan, T.N.[Tie-Niu],
Brief review of invariant texture analysis methods,
PR(35), No. 3, March 2002, pp. 735-747.
Elsevier DOI 0201
BibRef

Ferro, C.J.S.[Christopher J.S.], Warner, T.[Timothy],
Scale and Texture in Digital Image Classification,
PhEngRS(68), No. 1, January 2002, pp. 51-64. Simulated and actual data experiments were used to determine the effects various texture scales had upon a maximum-likelihood classifier and to suggest an approach that might aid in the selection of appropriate window sizes for texture feature extraction.
WWW Link. 0201
BibRef

Nielsen, M.[Mads], Hansen, L.K.[Lars Kai], Johansen, P.[Peter], Sporring, J.[Jon],
Guest Editorial: Special Issue on Statistics of Shapes and Textures,
JMIV(17), No. 2, September 2002, pp. 87-87.
DOI Link 0211
BibRef

Maillard, P.[Philippe],
Comparing Texture Analysis Methods through Classification,
PhEngRS(69), No. 4, April 2003, pp. 357-368. Three texture analysis methods, all based on different mathematical tools and all tested on high-resolution aerial photograph texture samples, are compared in dif ferent classification contexts, results are presented, and details of the experimental design for their comparison are explained.
WWW Link. 0304
BibRef

Xu, B.[Bing], Gong, P.[Peng], Seto, E.[Edmund], Spear, R.[Robert],
Comparison of Gray-Level Reduction and Different Texture Spectrum Encoding Methods for Land-Use Classification Using a Panchromatic Ikonos Image,
PhEngRS(69), No. 5, May 2003, pp. 529-536. There was little difference in classification accuracy among the three modified TS methods,
WWW Link. 0307
BibRef

Clausi, D.A.,
An analysis of co-occurrence texture statistics as a function of grey level quantization,
CanRS(28), No. 1, 2002, pp. 45-62.
HTML Version.
PDF File. BibRef 0200

Clausi, D.A.,
Comparison and fusion of co-occurrence, Gabor, and MRF texture features for classification of SAR sea ice imagery,
Atmosphere&Oceans(39), No. 4, 2001, pp. 183-194.
HTML Version.
PDF File.
See also K-means Iterative Fisher (KIF) unsupervised clustering algorithm applied to image texture segmentation. BibRef 0100

Clausi, D.A., Deng, H.[Huawu],
Design-Based Texture Feature Fusion Using Gabor Filters and Co-Occurrence Probabilities,
IP(14), No. 7, July 2005, pp. 925-936.
IEEE DOI 0506
BibRef
Earlier:
Feature fusion for image texture segmentation,
ICPR04(I: 580-583).
IEEE DOI 0409
BibRef

Clausi, D.A., Yue, B.[Bing],
Comparing Cooccurrence Probabilities and Markov Random Fields for Texture Analysis of SAR Sea Ice Imagery,
GeoRS(42), No. 1, January 2004, pp. 215-228.
IEEE Abstract. 0402
BibRef
Earlier:
Texture segmentation comparison using grey level co-occurrence probabilities and markov random fields,
ICPR04(I: 584-587).
IEEE DOI 0409
BibRef

Jobanputra, R.[Rishi], Clausi, D.A.[David A.],
Preserving boundaries for image texture segmentation using grey level co-occurring probabilities,
PR(39), No. 2, February 2006, pp. 234-245.
Elsevier DOI 0512
BibRef
Earlier:
Texture analysis using gaussian weighted grey level co-occurrence probabilities,
CRV04(51-57).
IEEE DOI 0408
BibRef

Chantler, M.J.[Mike J.], Van Gool, L.J.[Luc J.],
Editorial: Special Issue on 'Texture Analysis and Synthesis',
IJCV(62), No. 1-2, April-May 2005, pp. 5-5.
DOI Link 0411
BibRef

Petrou, M.,
Image Processing: Dealing with Texture,
Wiley2006, ISBN: 0-470-02628-6.
WWW Link. BibRef 0600

Mirmehdi, M.[Majid], Xie, X.H.[Xiang-Hua], Suri, J.[Jasjit],
Handbook of Texture Analysis,
World Scientific2008. ISBN 978-1-84816-115-3. Survey, Texture. Collects the basic texture approaches in one book. Buy this book: Handbook of Texture Analysis BibRef 0800

Kandaswamy, U., Schuckers, S.A., Adjeroh, D.A.,
Comparison of Texture Analysis Schemes Under Nonideal Conditions,
IP(20), No. 8, August 2011, pp. 2260-2275.
IEEE DOI 1108

See also Efficient Texture Analysis of SAR Imagery. BibRef

Kandaswamy, U., Adjeroh, D.A., Schuckers, S., Hanbury, A.,
Robust Color Texture Features Under Varying Illumination Conditions,
SMC-B(42), No. 1, February 2012, pp. 58-68.
IEEE DOI 1201
BibRef

Kandaswamy, U.,
Texture Content Based Successive Approximations for Image Compression and Recognition,
DICTA15(1-7)
IEEE DOI 1603
approximation theory BibRef

Hossain, S.[Shahera], Serikawa, S.[Seiichi],
Texture databases: A comprehensive survey,
PRL(34), No. 15, 2013, pp. 2007-2022.
Elsevier DOI 1309
Dataset, Texture. Survey, Texture Datasets. Texture. BibRef

Bianconi, F.[Francesco], Fernández, A.[Antonio],
An appendix to 'Texture databases: A comprehensive survey',
PRL(45), No. 1, 2014, pp. 33-38.
Elsevier DOI 1407
Texture BibRef

Li, L.[Liang], Asano, A.[Akira], Asano, C.M.[Chie Muraki], Okajima, K.[Katsunori],
Statistical quantification of the effects of viewing distance on texture perception,
JOSA-A(30), No. 7, July 2013, pp. 1394-1403.
WWW Link. 1309
Human visual system issues. BibRef

Pappas, T.N.[Thrasyvoulos N.],
Visual Signal Analysis: Focus on Texture Similarity,
SPIE(Newsroom), August 31, 2015
DOI Link 1511
A plenary talk from SPIE Optics + Photonics 2015. BibRef

Liu, L.[Li], Chen, J.[Jie], Fieguth, P.W.[Paul W.], Zhao, G.Y.[Guo-Ying], Chellappa, R.[Rama], Pietikäinen, M.[Matti],
From BoW to CNN: Two Decades of Texture Representation for Texture Classification,
IJCV(127), No. 1, January 2019, pp. 74-109.
Springer DOI 1901
BibRef

Kupidura, P.[Przemyslaw],
The Comparison of Different Methods of Texture Analysis for Their Efficacy for Land Use Classification in Satellite Imagery,
RS(11), No. 10, 2019, pp. xx-yy.
DOI Link 1906
BibRef

Porebski, A.[Alice], Alimoussa, M.[Mohamed], Vandenbroucke, N.[Nicolas],
Comparison of color imaging vs. hyperspectral imaging for texture classification,
PRL(161), 2022, pp. 115-121.
Elsevier DOI 2209
Texture representation, Color imaging, Hyperspectral imaging, Feature selection BibRef

Kupidura, P.[Przemyslaw], Lesisz, K.[Katarzyna],
The Impact of the Type and Spatial Resolution of a Source Image on the Effectiveness of Texture Analysis,
RS(15), No. 1, 2023, pp. xx-yy.
DOI Link 2301
BibRef

Gadermayr, M.[Michael], Liedlgruber, M.[Michael],
Problems in Distortion Corrected Texture Classification and the Impact of Scale and Interpolation,
CIAP13(I:513-522).
Springer DOI 1311
BibRef

Hegarty, J., Carswell, J.D.,
SAMATS: Texture extraction explained,
3DARCH09(xx-yy).
PDF File. 0902
Applied to 3D building descriptions. BibRef

Haindl, M.[Michal], Mikes, S.[Stanislav],
Texture segmentation benchmark,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Bai, Y.H.[Yoon Ho], Park, C.[Choonseog], Choe, Y.[Yoonsuck],
Relative advantage of touch over vision in the exploration of texture,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Chen, C.M.[Chih-Ming], Chen, C.C.[Chien-Chang], Chen, C.C.[Chaur-Chin],
A Comparison of Texture Features Based on SVM and SOM,
ICPR06(II: 630-633).
IEEE DOI 0609
BibRef

Martin, G., Pattichis, M.S.,
The characterization of scanning noise and quantization on texture feature analysis,
Southwest04(152-156).
IEEE DOI 0411
In medical screening application. BibRef

Singh, M., Singh, S.,
Spatial texture analysis: a comparative study,
ICPR02(I: 676-679).
IEEE DOI 0211
BibRef

Castano, R., Manduchi, R., Fox, J.,
Classification experiments on real-world texture,
EEMCV01(xx-yy). 0110
BibRef

Karkanis, S.A., Magoulas, G., Iakovidis, D.K., Karras, D., Maroulis, D.E.,
Evaluation of Textural Feature Extraction Schemes for Neural Network-based Interpretation of Regions in Medical Images,
ICIP01(I: 281-284).
IEEE DOI 0108

See also Detection of Lesions in Endoscopic Video Using Textural Descriptors on Wavelet Domain Supported by Artificial Neural Network Architectures. BibRef

Chang, K.I.[Kyong I.], Bowyer, K.W.[Kevin W.], Sivagurunath, M.[Munish],
Evaluation of Texture Segmentation Algorithms,
CVPR99(I: 294-299).
IEEE DOI BibRef 9900

Fleck, M.M.,
Texture: Plus Ca Change,
ECCV92(151-159).
Springer DOI BibRef 9200

Wang, P.S.P.[Patrick S.P.],
Activities of IAPR: TC-2, Learning, Representation and Visualization of Intelligent Pattern Recognition,
ICPR96(B9M.1). 9608
(USA) BibRef

Selkainaho, K., Parkkinen, J., Oja, E.,
Comparison of X2 and K Statistics in Finding Signal and Picture Periodicity,
ICPR88(II: 1221-1224).
IEEE DOI 8811
BibRef

Rosenfeld, A.,
Some Recent Developments in Texture Analysis,
PRIP79(618-622). BibRef 7900

Kreyszig, H.E.[Herbert E.],
Descriptors for Textures,
RBCV-TR-90-33, Toronto, July 1990. Master's thesis, a review of various statistical texture analysis methods. BibRef 9007

Chapter on 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture continues in
Texture Models, Analysis Techniques .