Automatic unsupervised texture segmentation using hidden Markov model

Authors:
Jia-Lin Chen;Amlan Kundu
Affiliations:
Department of Biophysical Science, SUNY at Buffalo, Buffalo, NY;Naval Command Control and Ocean Surveillance Center, RDT&E Division, San Diego, CA
Venue:
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: image and multidimensional signal processing - Volume V
Year:
1993

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Abstract

In this paper, we have described an automatic unsupervised texture segmentation scheme using hidden Markov model (HMM). In this scheme, each texture is modeled as one HMM. Thus, if there are M different textures present in an image, there are M distinct HMM's to be found and trained. Consequently, the unsupervised texture segmentation problem becomes an HMM based problem, where the appropriate number of HMM's, the associated model parameters and the discrimination among the HMM's are the foci of our scheme. The experimental results indicate that the present scheme compares favorably with respect to other successful schemes reported in the literature.