Fast Estimation of the Number of Texture Segments Using Co-occurrence Statistics
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abstract
Estimation of the number of clusters is an essential processing step for various applications. Existing approaches search for an optimal solution by computing and comparing a validity measure for all feasible configurations, and tend to under-estimate the number of clusters incorrectly. In this paper, we propose a fast and robust method to estimate the number of clusters without adopting the exhaustive search. Our scheme first extracts the relationship of neighboring features, and then uses this information to partition the clusters. The superb performance of the method is verified by the simulation results in determining the number of texture segments in the textured images.
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2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03).