@techreport{TR-IC-00-11,
  number = {IC-00-11},
  author = {Renato O. Stehling and Mario A. Nascimento and Alexandre X. Falcão},
  title = {Color-Shape Histograms for Image Representation and Retrieval},
  month = {July},
  year = {2000}, 
  institution = {Institute of Computing, University of Campinas},
  note = {In English, 20 pages.
    \par\selectlanguage{english}\textbf{Abstract}
      Color is a commonly used feature for realizing content-based image
      retrieval (CBIR).  In this context, this paper presents a new
      approach for CBIR which is based on well known and widely used
      color histograms.  Contrasting to previous approaches, such as
      using a single color histogram for the whole image, or local color
      histograms for a fixed number of image cells, the one we propose
      (named Color-Shape) uses a variable number of histograms,
      depending only on the actual number of colors present in the
      image, which our experiments have shown often to be low. Our
      experiments using a large set of heterogeneous images and
      pre-defined query/answer sets show that the Color-Shape approach
      offers good retrieval quality with relatively low space overhead,
      outperforming previous approaches. Furthermore, we also show that
      the proposed approach is very flexible in the sense that the user
      may easily tune it, in order to calibrate the trade-off between
      space overhead and retrieval effectiveness. For instance, when
      compared to using global color histograms, our approach can
      retrieve images 80{\%} more effectively, requiring 29 times more
      space for metadata. Although large, this space overhead is 55{\%}
      smaller than the overhead of more traditional partition-based
      approaches with equivalent parameters. On the other hand, it can
      be tuned to save 15{\%} in space requirements, when compared to
      storing a single global color histogram, while still being capable
      of yielding a 30{\%} more effective retrieval.
  }
}