Spatial Color Image Databases Summarization

This paper presents a finite discrete mixture model based on both the Dirichlet and the multinomial distributions to add spatial information to color histograms. The estimation of the parameters and the determination of the number of components in our model are based on the classification expectation-maximization approach and the integrated complete likelihood criterion, respectively. The developed model is applied with success for color images databases summarization.

[1]  Gérard Govaert,et al.  Assessing a Mixture Model for Clustering with the Integrated Completed Likelihood , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Geoffrey J. McLachlan,et al.  Finite Mixture Models , 2019, Annual Review of Statistics and Its Application.

[3]  David Kauchak,et al.  Modeling word burstiness using the Dirichlet distribution , 2005, ICML.

[4]  Nizar Bouguila,et al.  Unsupervised learning of a finite mixture model based on the Dirichlet distribution and its application , 2004, IEEE Transactions on Image Processing.

[5]  Jing Huang,et al.  Spatial Color Indexing and Applications , 2004, International Journal of Computer Vision.

[6]  Nizar Bouguila,et al.  A hybrid SEM algorithm for high-dimensional unsupervised learning using a finite generalized Dirichlet mixture , 2006, IEEE Transactions on Image Processing.

[7]  Marcel Worring,et al.  Content-Based Image Retrieval at the End of the Early Years , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Michael J. Swain,et al.  Color indexing , 1991, International Journal of Computer Vision.

[9]  P. Deb.  Finite Mixture Models , 2008 .