7.12 Co-occurrence Matrix Description Methods

Haralick, R.M., Shanmugam, K., and Dinstein, I.,
Textural Features for Image Classification,
SMC(3), No. 6, November 1973, pp. 610-621. BibRef 7311
And: CMetImAly77(141-152). Co-occurrence Matrix. Classic cooccurrence matrix computation and use. BibRef

Haralick, R.M., Shanmugam, K.,
Combined Spectral and Spatial Processing of ERTS Imagery Data,
RSE(3), No. 1, 1974, pp. 3-13. BibRef 7400

Haralick, R.M., and Dinstein, I.,
A Spatial Clustering Procedure for Multi-Image Data,
CirSys(22), No. 5, May 1975, pp. 440-450. BibRef 7505

Haralick, R.M.,
A Resolution Preserving Textural Transformation for Images,
CGPR75(51-61). BibRef 7500

Pressman, N.J.[Norman Jules],
Optical Texture Analysis for Automatic Cytology and Histology: A Markovian Approach,
Ph.D.EE, October 12, 1976. UCRL-52155, BibRef 7610 UCBLLL. Co-occurrence Matrix. Texture, Evaluation. Optical texture-spatial variation of gray levels, no general theory, but a systematic, comparative investigation of quantitative texture measures; Markovian - gray level transition probabilities (Haralick See also Textural Features for Image Classification. ); gradient; granulometric - characterize basic elements (Galloway - See also Texture Analysis Using Gray Level Run Lengths. ); transform (Fourier); using texture of a known region to characterize the region, not for segmentation; evaluation of step size (optimum) is necessary for each application. BibRef

Galloway, M.M.,
Texture Analysis Using Gray Level Run Lengths,
CGIP(4), No. 2, June 1975, pp. 172-179.
WWW Version. BibRef 7506

Davis, L.S., Mitiche, A.,
Edge Detection in Textures,
CGIP(12), No. 1, January 1980, pp. 25-39.
WWW Version. BibRef 8001
Earlier: A2, A1:
Theoretical Analysis of Edge Detection in Textures,
ICPR80(540-547). Texture segmentation: See also MITES: A Model Driven, Iterative Texture Segmentation Algorithm. BibRef

Davis, L.S., Johns, S., and Aggarwal, J.K.,
Texture Analysis Using Generalized Co-Occurrence Matrices,
PAMI(1), No. 3, July 1979, pp. 251-259. BibRef 7907
Earlier: PRIP78(313-318). BibRef
And: A1, A3 only: PRAI-78(185-189). BibRef

Davis, L.S., Clearman, M., and Aggarwal, J.K.,
An Empirical Evaluation of Generalized Cooccurrence Matrices,
PAMI(3), No. 2, March 1981, pp. 214-221. BibRef 8103
Earlier:
A Comparative Texture Classification Study Based on Generalized Co-occurrence Matrices,
IEEE Conferenceon Decision Control, Miami, December 12-14, 1979. BibRef

Davis, L.S.[Larry S.],
Polarograms: A New Tool for Image Texture Analysis,
PR(13), No. 3, 1981, pp. 219-223.
WWW Version. 0309 BibRef

Sun, C., Wee, W.G.,
Neighboring Gray Level Dependence Matrix for Texture Classification,
CVGIP(23), No. 3, September 1983, pp. 341-352.
WWW Version. BibRef 8309

Trivedi, M.M.[Mohan M.], Harlow, C.A.[Charles A.], Conners, R.W.[Richard W.], and Goh, S.[Semoon],
Object Detection Based on Gray Level Cooccurrence,
CVGIP(28), No. 2, November 1984, pp. 199-219.
WWW Version. Matching, Textures. BibRef 8411

Harlow, C.A.[Charles A.], Trivedi, M.M.[Mohan M.], and Conners, R.W.[Richard W.],
Use of Texture Operators in Image Segmentation,
OptEng(25), No. 11, November 1986, pp. 1200-1206. BibRef 8611

Gotlieb, C.C., and Kreyszig, H.E.,
Texture Descriptors Based on Co-occurrence Matrices,
CVGIP(51), No. 1, July 1990, pp. 70-86.
WWW Version. BibRef 9007

Picard, R.W., Elfadel, I.M.,
Structure of the Aura and Co-Occurrence Matrices for the Gibbs Texture Model,
JMIV(2), 1992, pp. 5-25. BibRef 9200

Elfadel, I.M.[Ibrahim M.], and Picard, R.W.[Rosalind W.],
Gibbs Random Fields, Cooccurrences, and Texture Modeling,
PAMI(16), No. 1, January 1994, pp. 24-37.
IEEE Abstract. IEEE Top Reference.
WWW Version. BibRef 9401
And: Vismod-204, 1992.
HTML Version. and
Postscript Version. BibRef

Picard, R.W., Elfadel, I.M., Pentland, A.P.,
Markov/Gibbs Texture Modeling: Aura Matrices and Temperature Effects,
CVPR91(371-377).
IEEE Abstract. IEEE Top Reference. BibRef 9100
And: Vismod-164, 1991.
HTML Version. and
Postscript Version. BibRef

Picard, R.W., Elfadel, I.M.,
On the Structure of Aura and Co-Occurrence Matrices for the Gibbs Texture Model,
Vismod-160, 1991.
HTML Version. and
Postscript Version. BibRef 9100

Elfadel, I.M., Picard, R.W.,
New Miscibility Measure Explains the Behavior of Grayscale Texture Synthesized By Gibbs Random Fields,
Vismod-159, 1991.
HTML Version. and
Postscript Version. BibRef 9100

Elfadel, I.M.[Ibrahim], and Yuille, A.L.,
Mean-Field Phase Transistions and Correlation Functions for Gibbs Random Fields,
JMIV(3), 1993, pp. 167-186. BibRef 9300

Picard, R.W.,
Structured Patterns From Random Fields,
Vismod200, 1992.
HTML Version. and
Postscript Version. BibRef 9200
And:
Random Field Texture Coding,
Vismod-185, 1992.
HTML Version. and
Postscript Version. BibRef
Earlier:
Gibbs Random Fields: Temperature and Parameter Analysis,
Vismod177, 1992.
HTML Version. and
Postscript Version. BibRef

Picard, R.W., Pentland, A.P.,
Markov/Gibbs Image Modeling: Temperature and Texture,
Vismod-175, 1991.
HTML Version. and
Postscript Version. BibRef 9100

Park, D.J., Nam, K.M., Park, R.H.,
Edge-Detection in Noisy Images Based on the Cooccurrence Matrix,
PR(27), No. 6, June 1994, pp. 765-775.
WWW Version. BibRef 9406

Hong, T.H., Dyer, C.R., and Rosenfeld, A.,
Texture Classification Using Gray Level Co-Occurrence Based on Edge Maxima,
SMC(10), 1980, pp. 158-163. BibRef 8000
And: A2, A1, A3 UMD-CS-TR-738, March 1979 See also TR 759, 779, 763. BibRef

Hong, T.H., Dyer, C.R., and Rosenfeld, A.,
Texture Primitive Extraction Using an Edge-Based Approach,
SMC(10), 1980, pp. 659-675. BibRef 8000

Hong, T.H., Wu, A.Y., and Rosenfeld, A.,
Feature Value Smoothing as an Aid in Texture Analysis,
SMC(10), 1980, pp. 519-524. BibRef 8000

Cohn-Sfetou, S.[Sorin],
Topics on Generalized Convolution and Fourier Transforms: Theory and Applications in Digital Signal Processing and System Theory,
Ph.D.Thesis (EE), McMaster Univ., Hamilton, Ontario, 1976. Convolution; transform on quadratic and multiplicative abelian groups, Walsh functions. BibRef 7600

Shirvaikar, M.V.[Mukul V.], and Trivedi, M.M.[Mohan M.],
Image Clutter Characterization for Object Detection in High Clutter Images,
OptEng(31), No. 12, December 1992, pp. 2628-2639. Target Recognition. BibRef 9212
Earlier:
Studies in Robust Approaches to Object Detection in High Clutter Background,
SPIE(1468), Applications of AI IX, Orlando, April 1991, pp. 52-59. BibRef

Shirvaikar, M.V.[Mukul V.], and Trivedi, M.M.[Mohan M.],
A Novel Unsupervised Multiresolution Texture Segmentation Approach,
SPIE(2223), Characterization and Propagation of Sources and Backgrounds IV, Orlando, FL, April 6-7, 1994. Gray level cooccurrence computations. BibRef 9404

Copeland, A.C., and Trivedi, M.M.,
Texture Perception in Humans and Computers: Models and Psychophysical Experiments,
SPIE(2742), 1996, pp. 436-446. BibRef 9600

Trivedi, M.M., and Shirvaikar, M.V.,
Quantitative Characterization of Image Clutter: Problems, Progress, and Promises,
SPIE(1967), Characterization, Propagation, and Simulation of Sources and Backgrounds, Orlando, FL, April 12-13, 1993. BibRef 9304

Harlow, C.A., Trivedi, M.M., and Conners, R.W.,
Texture Operators in Segmentation,
SPIE(548), Applications of Artificial Intelligence II, Arlington, VA, April 1985, pp. 10-18. Cooccurrence operators for aerial image segmentation. BibRef 8504

Muhamad, A.K.[Anwar K.], Deravi, F.[Farzin],
Neural Networks for the Classification of Image Texture,
EngAAI(7), No. 4, 1994, pp. 381-393. Neural Networks. BibRef 9400

Oja, E., Valkealahti, K.,
Cooccurrence Map: Quantizing Multidimensional Texture Histograms,
PRL(17), No. 7, June 10 1996, pp. 723-730. 9607 BibRef

Oja, E.[Erkki], Valkealahit, K.[Kimmo],
Reduced Multidimensional Histograms in Color Texture Description,
ICPR98(Vol II: 1057-1061).
IEEE DOI Reference 9808 BibRef

Valkealahti, K.[Kimmo], and Oja, E.[Erkki],
Reduced Multidimensional Texture Histograms,
SCIA97(xx-yy) 9705
HTML Version. BibRef

Kovalev, V.A., Petrou, M.,
Multidimensional Cooccurrence Matrices for Object Recognition and Matching,
GMIP(58), No. 3, May 1996, pp. 187-197. 9606 BibRef

Petrou, M., Mohanna, F., Kovalev, V.A.,
3D non-linear invisible boundary detection filters,
3DPVT04(970-978).
IEEE Abstract. IEEE Top Reference. 0412Huiman distinguish up to second order statistics. But tumors may not differ in second order. MRI analysis. BibRef

Petrou, M., Kovalev, V.A., Reichenbach, J.R.,
Three-Dimensional Nonlinear Invisible Boundary Detection,
IP(15), No. 10, October 2006, pp. 3020-3032.
IEEE DOI Reference 0609 BibRef

Ramana, K.V., Ramamoorthy, B.,
Statistical-Methods to Compare the Texture Features of Machined Surfaces,
PR(29), No. 9, September 1996, pp. 1447-1459.
WWW Version. Machined Surfaces. Co-occurrence Matrix. Run Length Code. BibRef 9609

Parkkinen, J., Selkainaho, K., Oja, E.,
Detecting Texture Periodicity from the Cooccurrence Matrix,
PRL(11), 1990, pp. 43-50. BibRef 9000

Valkealahti, K.[Kimmo], Oja, E.[Erkki],
Reduced Multidimensional Cooccurrence Histograms in Texture Classification,
PAMI(20), No. 1, January 1998, pp. 90-94.
IEEE Abstract. IEEE Top Reference.
WWW Version. 9803 BibRef

Valkealahti, K., Oja, E.,
Texture Classification with Single and Multiresolution Cooccurrence Maps,
PRAI(12), No. 4, June 1998, pp. 437-452. 9808 BibRef

Tang, X.,
Texture Information in Run-length Matrices,
IP(7), No. 11, November 1998, pp. 1602-1609.
IEEE DOI Reference BibRef 9811

Lee, J.C.M., Pong, T.C., Esterline, A.,
Enhancing Object Recognition Using Regency and Cooccurrence Heuristics,
PR(31), No. 9, September 1998, pp. 1319-1336.
WWW Version. 9808 BibRef

Soh, L.K., Tsatsoulis, C.,
Texture Analysis of SAR Sea Ice Imagery Using Gray Level Co-Occurrence Matrices,
GeoRS(37), No. 2, March 1999, pp. 780.
IEEE Top Reference. BibRef 9903

Soh, L.K., Tsatsoulis, C., Gineris, D., Bertoia, C.,
ARKTOS: An Intelligent System for SAR Sea Ice Image Classification,
GeoRS(42), No. 1, January 2004, pp. 229-248.
IEEE Abstract. IEEE Top Reference. 0402 BibRef

Carr, J.R., Pellon de Miranda, F.,
The Semivariogram in Comparison to the Co-Occurrence Matrix for Classification of Image Texture,
GeoRS(36), No. 6, November 1998, pp. 1945.
IEEE Top Reference. BibRef 9811

Chetverikov, D.[Dmitry],
Texture analysis using feature-based pairwise interaction maps,
PR(32), No. 3, March 1999, pp. 487-502.
WWW Version. BibRef 9903
Earlier:
Texture analysis using pairwise interaction maps,
CIAP97(I: 95-102).
WWW Version. 9709 BibRef
Earlier:
Structural Filtering with Texture Feature Based Interaction Maps: Fast Algorithms and Applications,
ICPR96(II: 795-799).
IEEE DOI Reference 9608(Hungarian Academy of Sciences, H) BibRef

Gimel'Farb, G.L.[Georgy L.],
Modeling image textures by Gibbs random fields,
PRL(20), No. 11-13, November 1999, pp. 1123-1132. 0001 BibRef

Gimel'Farb, G.L.[Georgy L.],
Image Textures and Gibbs Random Fields,
KluwerSeptember 1999, ISBN 0-7923-5961-5.
WWW Version. BibRef 9909

Gimel'Farb, G.L.,
Non-Markov Gibbs Texture Model with Multiple Pairwise Pixel Interactions,
ICPR96(II: 591-595).
IEEE DOI Reference 9608(V.M. Glushkov Institute of Cybernetics, UKR) BibRef

Gimel'Farb, G.L.[Georgy L.],
Texture Modelling and Segmenting by Multiple Pairwise Pixel Interactions,
ICIP96(III: 133-136).
IEEE DOI Reference BibRef 9600

Gimel'Farb, G.L.,
Gibbs Models for Bayesian Simulation and Segmentation of Piecewise-Uniform Textures,
ICPR96(II: 760-764).
IEEE DOI Reference 9608(V.M. Glushkov Institute of Cybernetics, UKR) BibRef

Lafarge, F., Gimel'farb, G.L.,
Texture Representation by Geometric Objects using a Jump-Diffusion Process,
BMVC08(xx-yy).
PDF Version. 0809 BibRef

Montiel, E.[Eugenia], Aguado, A.S.[Alberto S.], Nixon, M.S.[Mark S.],
Texture classification via conditional histograms,
PRL(26), No. 11, August 2005, pp. 1740-1751.
WWW Version. 0506 BibRef

Hammouche, K., Diaf, M., Postaire, J.G.,
A clustering method based on multidimensional texture analysis,
PR(39), No. 7, July 2006, pp. 1265-1277.
WWW Version. 0606Cluster analysis; Texture; Co-occurrence matrices; Feature selection BibRef

Vadivel, A., Sural, S.[Shamik], Majumdar, A.K.,
An Integrated Color and Intensity Co-occurrence Matrix,
PRL(28), No. 8, 1 June 2007, pp. 974-983.
WWW Version. 0704Co-occurrence matrix; HSV color space; ICICM; Image retrieval BibRef

Gelzinis, A., Verikas, A., Bacauskiene, M.,
Increasing the discrimination power of the co-occurrence matrix-based features,
PR(40), No. 9, September 2007, pp. 2367-2372.
WWW Version. 0705Image texture; Co-occurrence matrix; Support vector machine BibRef

Mirowski, P.W.[Piotr W.], Tetzlaff, D.M.[Daniel M.],
Retrieving scale from quasi-stationary images,
PRL(29), No. 6, 15 April 2008, pp. 754-767.
WWW Version. 0803Multi-scale; Rotation-guided; Texture characterization; Gray-Level Co-occurrence matrices; Quasi-stationary images BibRef

Partio, M.[Mari], Cramariuc, B.[Bogdan], Gabbouj, M.[Moncef],
An Ordinal Co-occurrence Matrix Framework for Texture Retrieval,
JIVP(2007), 2007, pp. xx-yy.
WWW Version. 0804 BibRef

de O. Bastos, L., Liatsis, P., Conci, A.,
Automatic texture segmentation based on k-means clustering and efficient calculation of co-occurrence features,
WSSIP08(141-144).
IEEE DOI Reference 0806 BibRef

Winter, M., Bischof, H.,
Binary Co-occurrences of Weak Descriptors,
BMVC07(xx-yy).
PDF Version. 0709 BibRef

Tsai, F.[Fuan], Chang, C.K.[Chun-Kai], Rau, J.Y.[Jian-Yeo], Lin, T.H.[Tang-Huang], Liu, G.R.[Gin-Ron],
3D Computation of Gray Level Co-occurrence in Hyperspectral Image Cubes,
EMMCVPR07(429-440).
Springer DOI Reference 0708 BibRef

Tahir, M.A., Bouridane, A., Kurugollu, E., Amira, A.,
Accelerating the Computation of GLCM and Haralick Texture Features on Reconfigurable Hardware,
ICIP04(V: 2857-2860).
IEEE DOI Reference 0505 BibRef

Partio, M., Cramariuc, B., Gabbouj, M.,
Block-based Ordinal Co-occurrence Matrices for Texture Similarity Evaluation,
ICIP05(I: 517-520).
IEEE DOI Reference 0512 BibRef
Earlier:
Texture similarity evaluation using ordinal co-occurrence,
ICIP04(III: 1537-1540).
IEEE DOI Reference 0505 BibRef

Schwartz, W.R., Pedrini, H.,
Textured Image Segmentation Based on Spatial Dependence using a Markov Random Field Model,
ICIP06(2449-2452). 0610
IEEE DOI Reference BibRef
Earlier:
Texture classification based on spatial dependence features using co-occurrence matrices and markov random fields,
ICIP04(I: 239-242).
IEEE DOI Reference 0505 BibRef

Zwiggelaar, R.,
Texture based segmentation: Automatic Selection of Co-occurrence Matrices,
ICPR04(I: 588-591).
IEEE DOI Reference 0409 BibRef

Hao, P.W.[Peng-Wei], Shi, Q.Q.[Qi-Qyun], Chen, Y.[Ying],
Co-histogram and its application in remote sensing image compression evaluation,
ICIP03(III: 177-180).
IEEE Abstract. IEEE Top Reference. 0312 BibRef

Metzler, V., Palm, C., Lehmann, T., Aach, T.,
Texture Classification of Graylevel Images by Multiscale Cross-cooccurrence Matrices,
ICPR00(Vol II: 549-552).
IEEE DOI Reference
HTML Version. 0009 BibRef

Andersen, J.D., Hansen, K.,
Analysis of Image Structure by Generalized Co-occurrence Matrices,
SCIA99(Image Analysis). BibRef 9900

Ojala, T., Pietikäinen, M., Kyllönen, J.,
Gray Level Cooccurrence Histograms via Learning Vector Quantization,
SCIA99(Neural Nets). BibRef 9900

Svalbe, I.D.[Imants D.], Evans, C.J.[Carolyn J.],
Localisation of Image Features Using Measures of Rank Distribution,
ICPR98(Vol I: 189-191).
IEEE DOI Reference 9808 BibRef

Hofmann, T., Puzicha, J.,
Mixture models for co-occurrence and histogram data,
ICPR98(Vol I: 192-194).
IEEE DOI Reference 0403 BibRef

Bello, F., Kitney, R.I.,
Co-Occurrence Based Texture Analysis Using Irregular Tessellations,
ICPR96(II: 780-784).
IEEE DOI Reference 9608(Imperial College of Science, UK) BibRef

Lohmann, G.,
Co-occurrence-based analysis and synthesis of textures,
ICPR94(A:449-453).
IEEE DOI Reference 9410 BibRef

Chapter on 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture continues in
Structural Methods for Texture Description .