Framework for texture classification and retrieval using scale invariant feature transform

Tuan Do, Antti Aikala, Olli Saarela

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

    2 Citations (Scopus)

    Abstract

    Texture images can be characterized with key features extracted from images. In this paper, the scale invariant feature transform (hereinafter SIFT) algorithm is utilized to generate local features for texture image classification. The local features are selected as inputs for texture classification framework. For each texture category, a texton dictionary is built based on the local features. To establish the texton dictionary, an adaptive mean shift clustering algorithm is run with all local features to generate key features (called textons) for texton dictionary. The texton dictionaries among texture categories are supposed be distinctive from each other to provide a highest performance in term of classification accuracy. A framework is proposed for classifying images into corresponding categories by matching their local features with textons from the texton dictionaries. This can be done by a histogram model of 'match' vectors versus texture categories. Finally, our texture image database and the Ponce texture database are used to test the proposed approach. The results indicate a potential of our proposed method based on high classification accuracies achieved. They are 100% with our testing database for both classification and retrieval and 92 % and 100% with Ponce database for classification and retrieval, respectively
    Original languageEnglish
    Title of host publicationProceedings
    Subtitle of host publicationNinth International Joint Conference on Computer Science and Software Engineering 2012, JCSSE 2012
    PublisherIEEE Institute of Electrical and Electronic Engineers
    Pages289-293
    ISBN (Electronic)978-1-4673-1921-8
    ISBN (Print)978-1-4673-1920-1
    DOIs
    Publication statusPublished - 2012
    MoE publication typeNot Eligible
    EventNinth International Joint Conference on Computer Science and Software Engineering 2012, JCSSE'12 - Bangkok, Thailand
    Duration: 30 May 20121 Jun 2012

    Conference

    ConferenceNinth International Joint Conference on Computer Science and Software Engineering 2012, JCSSE'12
    Abbreviated titleJCSSE 2012
    CountryThailand
    CityBangkok
    Period30/05/121/06/12

    Keywords

    • SIFT
    • local feature
    • adaptive mean shift clustering
    • texton
    • texton dictionary

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