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Evaluation of Salient Point Techniques

  • N. Sebe
  • Q. Tian
  • E. Loupias
  • M. Lew
  • T. Huang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2383)

Abstract

In image retrieval, global features related to color or texture are commonly used to describe the image content. The problem with this approach is that these global features cannot capture all parts of the image having different characteristics. Therefore, local computation of image information is necessary. By using salient points to represent local information, more discriminative features can be computed. In this paper we compare a wavelet-based salient point extraction algorithm with two corner detectors using the criteria: repeatability rate and information content. We also show that extracting color and texture information in the locations given by our salient points provides significantly improved results in terms of retrieval accuracy, computational complexity, and storage space of feature vectors as compared to global feature approaches.

Keywords

Feature Vector Image Retrieval Salient Point Texture Region Retrieval Accuracy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • N. Sebe
    • 1
  • Q. Tian
    • 2
  • E. Loupias
    • 3
  • M. Lew
    • 1
  • T. Huang
    • 2
  1. 1.LIACSLeiden UniversityLeidenThe Netherlands
  2. 2.Beckman InstituteUniversity of Illinois at Urbana-ChampaignUSA
  3. 3.Laboratoire Reconnaissance de Formes et VisionINSA-LyonFrance

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