Image-to-image retrieval using computationally learned bases and color information

Yasuo Matsuyama*, Fuminori Ohashi, Fumiaki Horiike, Tomohiro Nakamura, Shun'ichi Honma, Naoto Katsumata, Yuuki Hoshino

*この研究の対応する著者

    研究成果: Conference contribution

    抄録

    New methods for joint compression and Image-to-Image retrieval (I2I retrieval) are presented. The novelty exists in the usage of computationally learned image bases besides color distributions. The bases are obtained by the Principal Component Analysis and/or the Independent Component Analysis. On the image compression, PCA and ICA outperform the JPEG's DCT. This superiority holds even if the bases and superposition coefficients are quantized and encoded. On the I2I retrieval, the precision-recall curve is used to measure the performance. It is found that adding the basis information always increases the baseline ability of the color information. Besides the retrieval evaluation, a unified image format called RIM (Retrieval-aware IMage format) for effective packing of codewords including bases is specified. Furthermore, an image search viewer called Wisvi (Waseda Image Search VIewer) is developed and exploited. A β-version of all source codes can be down-loaded from a web site given in the text.

    本文言語English
    ホスト出版物のタイトルIEEE International Conference on Neural Networks - Conference Proceedings
    ページ546-551
    ページ数6
    DOI
    出版ステータスPublished - 2007
    イベント2007 International Joint Conference on Neural Networks, IJCNN 2007 - Orlando, FL
    継続期間: 2007 8 122007 8 17

    Other

    Other2007 International Joint Conference on Neural Networks, IJCNN 2007
    CityOrlando, FL
    Period07/8/1207/8/17

    ASJC Scopus subject areas

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