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Unsupervised Segmentation of Natural Images via Lossy Data Compression

Authors:
Yang, Allen Y.
Wright, John
Sastry, S. Shankar
Ma, Yi
Technical Report Identifier: EECS-2006-195
December 28, 2006

Abstract: In this paper, we cast natural-image segmentation as a problem of clustering texure features as multivariate mixed data. We model the distribution of the texture features using a mixture of Gaussian distributions. However, unlike most existing clustering methods, we allow the mixture components to be degenerate or nearly-degenerate. We contend that this assumption is particularly important for mid-level image segmentation, where degeneracy is typically introduced by using a common feature representation for different textures. We show that such a mixture distribution can be effectively segmented by a simple agglomerative clustering algorithm derived from a lossy data compression approach. Using simple fixed-size Gaussian windows as texture features, the algorithm segments an image by minimizing the overall coding length of all the feature vectors. In terms of a variety of performance indices, our algorithm compares favorably against other well-known image segmentation methods on the Berkeley image database.