Electrical Engineering
      and Computer Sciences

Electrical Engineering and Computer Sciences

COLLEGE OF ENGINEERING

UC Berkeley

Unsupervised Segmentation of Natural Images via Lossy Data Compression

Allen Y. Yang, John Wright, S. Shankar Sastry and Yi Ma

EECS Department
University of California, Berkeley
Technical Report No. UCB/EECS-2006-195
December 28, 2006

http://www.eecs.berkeley.edu/Pubs/TechRpts/2006/EECS-2006-195.pdf

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.


BibTeX citation:

@techreport{Yang:EECS-2006-195,
    Author = {Yang, Allen Y. and Wright, John and Sastry, S. Shankar and Ma, Yi},
    Title = {Unsupervised Segmentation of Natural Images via Lossy Data Compression},
    Institution = {EECS Department, University of California, Berkeley},
    Year = {2006},
    Month = {Dec},
    URL = {http://www.eecs.berkeley.edu/Pubs/TechRpts/2006/EECS-2006-195.html},
    Number = {UCB/EECS-2006-195},
    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.}
}

EndNote citation:

%0 Report
%A Yang, Allen Y.
%A Wright, John
%A Sastry, S. Shankar
%A Ma, Yi
%T Unsupervised Segmentation of Natural Images via Lossy Data Compression
%I EECS Department, University of California, Berkeley
%D 2006
%8 December 28
%@ UCB/EECS-2006-195
%U http://www.eecs.berkeley.edu/Pubs/TechRpts/2006/EECS-2006-195.html
%F Yang:EECS-2006-195