Parametric Distributional Clustering for Image Segmentation
- 2.5k Downloads
Unsupervised Image Segmentation is one of the central issues in Computer Vision. From the viewpoint of exploratory data analysis, segmentation can be formulated as a clustering problem in which pixels or small image patches are grouped together based on local feature information. In this contribution, parametrical distributional clustering (PDC) is presented as a novel approach to image segmentation. In contrast to noise sensitive point measurements, local distributions of image features provide a statistically robust description of the local image properties. The segmentation technique is formulated as a generative model in the maximum likelihood framework. Moreover, there exists an insightful connection to the novel information theoretic concept of the Information Bottleneck (Tishby et al. ), which emphasizes the compromise between efficient coding of an image and preservation of characteristic information in the measured feature distributions.
The search for good grouping solutions is posed as an optimization problem, which is solved by deterministic annealing techniques. In order to further increase the computational efficiency of the resulting segmentation algorithm, a multi-scale optimization scheme is developed. Finally, the performance of the novel model is demonstrated by segmentation of color images from the Corel data base.
KeywordsImage Segmentation Clustering Maximum Likelihood Information Theory
- 2.Thomas M. Cover and Joy A. Thomas. Elements of Information Theory. John Wiley & Sons, 1991.Google Scholar
- 3.I. Csizàr and G. Tusnady. Information geometry and alternating minimization procedures. In E. J. Dudewicz et al, editor, Recent Results in Estimation Theory and Related Topics, Statistics and Decisions, Supplement Issue No. 1. Oldenbourg, 1984.Google Scholar
- 8.D. Martin, C. Fowlkes, D. Tal, and J. Malik. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In Proc. ICCV’01, 2001.Google Scholar
- 9.R. M Neal and G. E. Hinton. A view of the EM algorithm that justifies incremental, sparse, and other variants. In M. I. Jordan, editor, Learning in Graphical Models. MIT Press, 1999.Google Scholar
- 10.F. Pereira, N. Tishby, and L. Lee. Distributional clustering of english words. In 30th International Meeting of the Association of Computational Linguistics, pages 183–190, Columbus, Ohio, 1993.Google Scholar
- 13.J. Puzicha, T. Hofmann, and J. M. Buhmann. A theory of proximity based clustering: Structure detection by optimization. Pattern Recognition, 2000.Google Scholar
- 17.N. Tishby, F. Pereira, and W. Bialek. The information bottleneck method. In Proc. of the 37th annual Allerton Conference on Communication, Control, and Computing, 1999.Google Scholar