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Multimedia Systems

, Volume 8, Issue 6, pp 495–511 | Cite as

Analyzing scenery images by monotonic tree

  • Yuqing Song
  • Aidong Zhang
Special issue on Content-Based image Retrieval

Abstract.

Content-based image retrieval (CBIR) has been an active research area in the last ten years, and a variety of techniques have been developed. However, retrieving images on the basis of low-level features has proven unsatisfactory, and new techniques are needed to support high-level queries. Research efforts are needed to bridge the gap between high-level semantics and low-level features. In this paper, we present a novel approach to support semantics-based image retrieval. Our approach is based on the monotonic tree, a derivation of the contour tree for use with discrete data. The structural elements of an image are modeled as branches (or subtrees) of the monotonic tree. These structural elements are classified and clustered on the basis of such properties as color, spatial location, harshness and shape. Each cluster corresponds to some semantic feature. This scheme is applied to the analysis and retrieval of scenery images. Comparisons of experimental results of this approach with conventional techniques using low-level features demonstrate the effectiveness of our approach.

Key words: Content-based image retrieval – Image feature extraction – Annotation – Semantics retrieval – Monotonic tree 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Yuqing Song
    • 1
  • Aidong Zhang
    • 1
  1. 1.Department of Computer and Information Science, The University of Michigan at Dearborn, 4901 Evergreen, Dearborn, Michigan 48128, USA US

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