7.12 Co-occurrence Matrix Description Methods

Chapter Contents (Back)
Co-occurrence Matrix. Texture, Co-occurrence.

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 Link 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. 0412
Huiman 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 Link 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 Link 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 Link 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 Link 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 Link BibRef 9600

Gimel'Farb, G.L.,
Gibbs Models for Bayesian Simulation and Segmentation of Piecewise-Uniform Textures,
ICPR96(II: 760-764).
IEEE DOI Link 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. 0606
Cluster 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. 0704
Co-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. 0705
Image 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. 0803
Multi-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

Chalumeau, T., da Fontoura Costa, L.[Luciano], Laligant, O., Meriaudeau, F.,
Complex networks: Application for Texture Characterization and Classification,
ELCVIA(7), No. 3, 2008, pp. xx-yy.
WWW Version. 0909
Networks of single pixels. BibRef

Ni, B.B.[Bing-Bing], Yan, S.C.[Shui-Cheng], Kassim, A.A.[Ashraf A.],
Contextualizing histogram,
CVPR09(1682-1689).
IEEE DOI Link 0906
Incorporate context into histogram analysis. Co-occurrence features. BibRef

Cheong, M., Loke, K.S.,
An approach to texture-based image recognition by deconstructing multispectral co-occurrence matrices using Tchebichef orthogonal polynomials,
ICPR08(1-4).
IEEE DOI Link 0812
BibRef

Porebski, A.[Alice], Vandenbroucke, N.[Nicolas], Macaire, L.[Ludovic],
Haralick feature extraction from LBP images for color texture classification,
IPTA08(1-8).
IEEE DOI Link 0811
BibRef