Content Based Image Retrieval Using Visual-Words Distribution Entropy

  • Savvas A. Chatzichristofis
  • Chryssanthi Iakovidou
  • Yiannis S. Boutalis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6930)

Abstract

Bag-of-visual-words (BOVW) is a representation of images which is built using a large set of local features. To date, the experimental results presented in the literature have shown that this approach achieves high retrieval scores in several benchmarking image databases because of their ability to recognize objects and retrieve near-duplicate (to the query) images. In this paper, we propose a novel method that fuses the idea of inserting the spatial relationship of the visual words in an image with the conventional Visual Words method. Incorporating the visual distribution entropy leads to a robust scale invariant descriptor. The experimental results show that the proposed method demonstrates better performance than the classic Visual Words approach, while it also outperforms several other descriptors from the literature.

Keywords

Image Retrieval Visual Word Query Image Mean Average Precision CBIR System 
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 2011

Authors and Affiliations

  • Savvas A. Chatzichristofis
    • 1
  • Chryssanthi Iakovidou
    • 1
  • Yiannis S. Boutalis
    • 1
  1. 1.Department of Electrical and Computer EngineeringDemocritus University of ThraceXanthiGreece

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