Unsupervised texture segmentation using multiresolution hybrid genetic algorithm

Chang Tsun Li, Randy Chiao

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

    10 Citations (Scopus)

    Abstract

    This work approaches the texture segmentation problem by incorporating genetic algorithm and k-mean clustering method within a multiresolution structure. First, a quad-tree structure is constructed and the input image is partition into blocks at different resolution levels. Texture features are then extracted from each block. Based on the texture features, a hybrid genetic algorithm is employed to perform the segmentation. The crossover operator of traditional genetic algorithm is replaced with k-means clustering method while the mutate and select operators are adopted. In the final step, the boundaries and the segmentation result of the current resolution level are propagated down to the next level to act as contextual constraints and the initial configuration of the next level, respectively.

    Original languageEnglish
    Title of host publicationIEEE International Conference on Image Processing
    Pages1033-1036
    Number of pages4
    Volume2
    Publication statusPublished - 2003
    EventProceedings: 2003 International Conference on Image Processing, ICIP-2003 - Barcelona, Spain
    Duration: 14 Sep 200317 Sep 2003

    Conference

    ConferenceProceedings: 2003 International Conference on Image Processing, ICIP-2003
    Country/TerritorySpain
    CityBarcelona
    Period14/09/0317/09/03

    Fingerprint

    Dive into the research topics of 'Unsupervised texture segmentation using multiresolution hybrid genetic algorithm'. Together they form a unique fingerprint.

    Cite this