A random field approach to unsupervised texture image segmentation

Chang Tsun Li, Roland Wilson

    Research output: Book chapter/Published conference paperConference paperpeer-review

    Abstract

    An unsupervised random field approach, which involves local and long range information in determining the class of target image blocks, in texture segmentation problem is introduced in this work. Like Markov random field (MRFs) approaches, the proposed method treats each of the image blocks as a site and attempts to assign an optimal class label to each of it. Unlike MRF,s, which involve only local information extracted from a small neighborhood, in addition to the local neighbors, our method allows a few long range blocks to be involved in the labeling process, in an attempt to alleviate the problem of assigning different class labels to disjoint regions of the same texture and the problem of over-segmentation due to the lack of long range interaction among the neighbors and the distant blocks. The proposed method requires no a priori knowledge of the number and types of region.

    Original languageEnglish
    Title of host publicationProceedings of the 5th IASTED International Conference on Visualization, Imaging, and Image Processing, VIIP 2005
    Pages406-411
    Number of pages6
    Publication statusPublished - 2005
    Event5th IASTED International Conference on Visualization, Imaging, and Image Processing, VIIP 2005 - Benidorm, Spain
    Duration: 07 Sep 200509 Sep 2005

    Conference

    Conference5th IASTED International Conference on Visualization, Imaging, and Image Processing, VIIP 2005
    Country/TerritorySpain
    CityBenidorm
    Period07/09/0509/09/05

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