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Supporting Content-Based Retrieval in Large Image Database Systems

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Abstract

In this paper, we investigate approaches to supporting effective and efficient retrieval of image data based on content. We firstintroduce an effective block-oriented image decomposition structure which can be used to represent image content inimage database systems. We then discuss theapplication of this image data model to content-based image retrieval.Using wavelet transforms to extract image features,significant content features can be extracted from image datathrough decorrelating the data in their pixel format into frequency domain. Feature vectors ofimages can then be constructed. Content-based image retrievalis performed by comparing the feature vectors of the query imageand the decomposed segments in database images.Our experimental analysis illustrates that the proposed block-oriented image representationoffers a novel decomposition structure to be used tofacilitate effective and efficient image retrieval.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, State University of New York at Buffalo, Buffalo, NY, 14260

    Edward Remias

  2. Department of Computer Science, State University of New York at Buffalo, Buffalo, NY, 14260

    Gholamhosein Sheikholeslami & Aidong Zhang

  3. Xerox Research Center, Webster, NY, 14580

    Tanveer Fathima Syeda-Mahmood

Authors
  1. Edward Remias
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  2. Gholamhosein Sheikholeslami
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  3. Aidong Zhang
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  4. Tanveer Fathima Syeda-Mahmood
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Remias, E., Sheikholeslami, G., Zhang, A. et al. Supporting Content-Based Retrieval in Large Image Database Systems. Multimedia Tools and Applications 4, 153–170 (1997). https://doi.org/10.1023/A:1009614331352

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