Theory of keyblock-based image retrieval

  • Authors:

  • Lei Zhu;Al Bing Rao;Aldong Zhang

  • Affiliations:

  • State University of New York at Buffalo;State University of New York at Buffalo;State University of New York at Buffalo

  • Venue:
  • ACM Transactions on Information Systems (TOIS)
  • Year:
  • 2002

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Abstract

The success of text-based retrieval motivates us to investigate analogous techniques which can support the querying and browsing of image data. However, images differ significantly from text both syntactically and semantically in their mode of representing and expressing information. Thus, the generalization of information retrieval from the text domain to the image domain is non-trivial. This paper presents a framework for information retrieval in the image domain which supports content-based querying and browsing of images. A critical first step to establishing such a framework is to construct a codebook of "keywords" for images which is analogous to the dictionary for text documents. We refer to such "keywords" in the image domain as "keyblocks." In this paper, we first present various approaches to generating a codebook containing keyblocks at different resolutions. Then we present a keyblock-based approach to content-based image retrieval. In this approach, each image is encoded as a set of one-dimensional index codes linked to the keyblocks in the codebook, analogous to considering a text document as a linear list of keywords. Generalizing upon text-based information retrieval methods, we then offer various techniques for image-based information retrieval. By comparing the performance of this approach with conventional techniques using color and texture features, we demonstrate the effectiveness of the keyblock-based approach to content-based image retrieval.