Similar-image retrieval systems using ICA and PCA bases

Naoto Katsumata, Yasuo Matsuyama

    研究成果: Conference contribution

    3 被引用数 (Scopus)

    抄録

    Similar-image retrieval systems are presented and evaluated. The new systems directly use image bases via ICA (Independent Component Analysis) and PCA (Principal Component Analysis). These bases can extract source image's information which is viable to define similarity measures. But, the indeterminacy on amplitude and permutation exists. In this paper, similarity measures which can absorb such indeterminacy are presented. Then, carefully designed opinion tests are carried out to compare the new systems' ability with existing ones. The compatibility of color spaces such as RGB, YIQ, and HSV is also examined. By these massive tests, {ICA, HSV} is judged the best. The resulting system is thus proved to be highly competent at the similar-image retrieval.

    本文言語English
    ホスト出版物のタイトルProceedings of the International Joint Conference on Neural Networks
    ページ1229-1234
    ページ数6
    2
    DOI
    出版ステータスPublished - 2005
    イベントInternational Joint Conference on Neural Networks, IJCNN 2005 - Montreal, QC
    継続期間: 2005 7 312005 8 4

    Other

    OtherInternational Joint Conference on Neural Networks, IJCNN 2005
    CityMontreal, QC
    Period05/7/3105/8/4

    ASJC Scopus subject areas

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