Similar-image retrieval systems using ICA and PCA bases

Naoto Katsumata, Yasuo Matsuyama

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    3 Citations (Scopus)

    Abstract

    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.

    Original languageEnglish
    Title of host publicationProceedings of the International Joint Conference on Neural Networks
    Pages1229-1234
    Number of pages6
    Volume2
    DOIs
    Publication statusPublished - 2005
    EventInternational Joint Conference on Neural Networks, IJCNN 2005 - Montreal, QC
    Duration: 2005 Jul 312005 Aug 4

    Other

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

      Fingerprint

    ASJC Scopus subject areas

    • Software

    Cite this

    Katsumata, N., & Matsuyama, Y. (2005). Similar-image retrieval systems using ICA and PCA bases. In Proceedings of the International Joint Conference on Neural Networks (Vol. 2, pp. 1229-1234). [1556029] https://doi.org/10.1109/IJCNN.2005.1556029