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

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

    Abstract

    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.

    Original languageEnglish
    Title of host publicationIEEE International Conference on Neural Networks - Conference Proceedings
    Pages546-551
    Number of pages6
    DOIs
    Publication statusPublished - 2007
    Event2007 International Joint Conference on Neural Networks, IJCNN 2007 - Orlando, FL
    Duration: 2007 Aug 122007 Aug 17

    Other

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

    ASJC Scopus subject areas

    • Software

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  • Cite this

    Matsuyama, Y., Ohashi, F., Horiike, F., Nakamura, T., Honma, S., Katsumata, N., & Hoshino, Y. (2007). Image-to-image retrieval using computationally learned bases and color information. In IEEE International Conference on Neural Networks - Conference Proceedings (pp. 546-551). [4371015] https://doi.org/10.1109/IJCNN.2007.4371015