Is there a best color space for color image characterization or representation based on Multivariate Gaussian Mixture Model?

Computer Vision and Image Understanding

Volumn 113, Issue 8, 2009, Pages 867-877

  Alata, Olivier ^a   Quintard, Ludovic ^a  

^a UNIVERSITÉ DE POITIERS   (France)

Author keywords

Color spaces; EM algorithm; Histogram; Information Criteria; Model selection; Multivariate Gaussian Mixture Model; Psychovisual distance; SEM algorithm

Indexed keywords

COLOR SPACES; EM ALGORITHM; HISTOGRAM; INFORMATION CRITERIA; MODEL SELECTION; MULTIVARIATE GAUSSIAN MIXTURE MODEL; PSYCHOVISUAL DISTANCE; SEM ALGORITHM;

ALGORITHMS; COLOR IMAGE PROCESSING; COMMUNICATION CHANNELS (INFORMATION THEORY); IMAGE ENHANCEMENT; MIXTURES; OBJECT RECOGNITION; PARAMETER ESTIMATION;

COLOR;

EID: 67349083320     PISSN: 10773142     EISSN: 1090235X     Source Type: Journal    
DOI: 10.1016/j.cviu.2009.03.001     Document Type: Article

References (32)

1. Celeux G., and Govaert G. Gaussian parsimonious clustering models. Pattern Recognition 28 5 (1995) 781-793
2. Biernacki C., Celeux G., and Govaert G. Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22 7 (2000) 719-725
3. Liua X., Fua H., and Jiaa Y. Gaussian mixture modeling and learning of neighboring characters for multilingual text extraction in images. Pattern Recognition 41 2 (2008) 484-493
4. M.-H. Yang, N. Ahuja, Gaussian mixture model for human skin color and its applications in image and video databases, in: Proc. of the SPIE Storage and Retrieval for Image and Video Databases, vol. 3656, 1999, pp. 45-466.
5. Permuter H., Francos J., and Jermyn I. A study of Gaussian mixture models of color and texture features for image classification and segmentation. Pattern Recognition 39 4 (2006) 695-706
6. Y. Wu, X. Yang, K.L. Chan, Unsupervised color image segmentation based on Gaussian mixture model, in: Proceedings of the 2003 Joint Conference of the Fourth International Conference on Information, Communications and Signal Processing and the Fourth Pacific Rim Conference on Multimedia, vol. 1, 2003, pp. 541-544.
7. Yuzhong W., Jie Y., and Yue Z. Color-texture segmentation using JSEG based on Gaussian mixture modeling. Journal of Systems Engineering and Electronics 17 1 (2006) 24-29
8. R. Hammoud, R. Mohr, Gaussian mixture densities for indexing of localized objects in a video sequence, Tech. Rep. RR-3905, INRIA, March 2000.
9. Carson C., Belongie S., Greenspan H., and Malik J. Blobworld: image segmentation using expectation-maximization and its application to image querying. IEEE Transactions on Pattern Analysis and Machine Intelligence 24 8 (2002) 1026-1038
10. A. Hanbury, J. Serra, A 3D-polar coordinate colour representation suitable for image analysis, Tech. Rep. TR-77, PRIP, 2002.
11. Luo M.R., Cui G., and Rigg B. The development of the CIE 2000 colour-difference formula: CIEDE2000. Color Research and Application 26 5 (2001) 340-350
12. Sharma G., Wu W., and Dalal E.N. The CIEDE2000 colour-difference formula: implementation notes, supplementary test data, and mathematical observations. Color Research and Application 30 (2005) 21-30
13. Berns R.S., Alman D.H., Reniff L., Snyder G.D., and Balonon-Rosen M. Visual determination of suprathreshold color difference tolerances using probit analysis. Color Research and Application 16 (1991) 297-316
14. Alman D.H., Berns R.S., Snyder G.D., and Larsen W.A. Performance testing of color difference metric using a color tolerance dataset. Color Research and Application 14 (1989) 139-151
15. Bozdogan H. Choosing the number of component clusters in the mixture-model using a new informational complexity criterion of the inverse-Fisher information matrix. In: Opitz O., Lausen B., and Klar R. (Eds). Information and Classification vol. 2 (1993), Springer, Heidelberg 40-54
16. Dempster A., Laird N., and Rubin D. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society 39 (1977) 1-38
17. Figueiredo M., and Jain A.K. Unsupervised learning of finite mixture models. IEEE Transactions on Pattern Analysis and Machine Intelligence 24 3 (2002) 381-396
18. Biernacki C., Celeux G., and Govaert G. Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models. Computational Statistics and Data Analysis 41 (2003) 561-575
19. Richardson S., and Green P. On Bayesian analysis of mixtures with an unknown number of components. Journal of the Royal Statistical Society 59 Series B (1997) 731-792
20. Liang R.J.Z., and Coleman R. Parameter estimation of finite mixtures using the EM algorithm and information criteria with application to medical image processing. IEEE Transactions on Nuclear Science 39 4 (1992) 1126-1133
21. Celeux G., and Diebolt J. The SEM Algorithm: a Probabilistic Teacher Algorithm derived from the EM Algorithm for the mixture problem. Computational Statistics Quarterly 2 1 (1985) 73-82
22. Akaike H. A new look at the statistical model identification. IEEE Transactions on Automatic Control AC-19 6 (1974) 716-723
23. Schwarz G. Estimating the dimension of a model. The Annals of Statistics 6 (1978) 461-464
24. Rissanen J. Modeling by shortest data description. Automatica 14 (1978) 465-471
25. Rissanen J. Stochastic complexity and modeling. The Annals of Statistics 14 3 (1986) 1080-1100
26. C. Olivier, F. Jouzel, A.E. Matouat, Choice of the number of component clusters in mixture models by information criteria, in: Proc. of the Vision Interface, 1999, pp. 74-81.
27. Celeux G., Forbes F., Robert C., and Titterington D. Deviance information criteria for missing data models. Bayesian Analysis 1 4 (2006) 651-980
28. Wang H.X., Luo B., and Wei S. Estimation for the number of components in a mixture model using stepwise split-and-merge EM algorithm. Pattern Recognition Letters 25 16 (2004) 1799-1809
29. El-Matouat A., and Hallin M. Order selection stochastic complexity and Kullback-Leibler information. In: Robinson P.M., and Rosenblatt M. (Eds). Time Series Analysis vol. 2 (1996), Springer, New York 291-299
30. Alata O., and Ramananjarasoa C. Unsupervised textured image segmentation using 2-d quarter plane autoregressive model with four prediction supports. Pattern Recognition Letters 26 (2005) 1069-1081
31. Ruanaidh J.K.O., and Fitzgerald W.J. Numerical Bayesian Methods Applied to Signal Processing. Statistics and computing (1996), Springer, New York
32. Hall P., and Hannan E.J. On stochastic complexity and nonparametric density estimation. Biometrika 75 4 (1988) 705-714