 Content based image retrieval and information theory: A general approachJohn Zachary, S. S. Iyengar and Jacob Barhen Journal of the American Society for Information Science and Technology, 2001, vol. 52, issue 10, 840-852 Abstract: A fundamental aspect of content‐based image retrieval (CBIR) is the extraction and the representation of a visual feature that is an effective discriminant between pairs of images. Among the many visual features that have been studied, the distribution of color pixels in an image is the most common visual feature studied. The standard representation of color for content‐based indexing in image databases is the color histogram. Vector‐based distance functions are used to compute the similarity between two images as the distance between points in the color histogram space. This paper proposes an alternative real valued representation of color based on the information theoretic concept of entropy. A theoretical presentation of image entropy is accompanied by a practical description of the merits and limitations of image entropy compared to color histograms. Specifically, the L1 norm for color histograms is shown to provide an upper bound on the difference between image entropy values. Our initial results suggest that image entropy is a promising approach to image description and representation. Date: 2001 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:bla:jamist:v:52:y:2001:i:10:p:840-852 Ordering information: This journal article can be ordered from Access Statistics for this article More articles in Journal of the American Society for Information Science and Technology from Association for Information Science & Technology |
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