Evaluating Content-Based Image Retrieval by Combining Color and Wavelet Features in a Region Based Scheme

  • Fernanda Ramos
  • Herman Martins Gomes
  • Díbio Leandro Borges
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3773)

Abstract

Content description and representation are still challenging issues for the design and management of content-based image retrieval systems. This work proposes to derive content descriptors of color images by wavelet coding and indexing of the HSV (Hue, Saturation, Value) channels. An efficient scheme for this problem has to trade between being translation and rotation invariant, fast and accurate at the same time. Based on a diverse and difficult database of 1020 color images, and a strong experimental protocol we propose a method that first divides an image into 9 rectangular regions (i.e. zoning), second it applies a wavelet transformation in each of the HSV channels. A subset of the approximation and of detail coefficients of each set is then selected. A similarity measure based on histogram intersection followed by vector distance computation for the 9 regions then evaluates and ranks the closest images of the database by content. In this paper we give the details of the this new approach and show promising results upon extensive experiments performed in our lab.

Keywords

Image Retrieval Wavelet Coefficient Query Image Detail Coefficient Wavelet Feature 
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 2005

Authors and Affiliations

  • Fernanda Ramos
    • 1
  • Herman Martins Gomes
    • 2
  • Díbio Leandro Borges
    • 3
  1. 1.Faculdade de Filosofia, Ciências e Letras de PalmasPalmasBrazil
  2. 2.Departamento de Sistemas e ComputaçãoUFCG – Universidade Federal de Campina GrandeCampina GrandeBrazil
  3. 3.BIOSOLOGoiâniaBrazil

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