Image retrieval based on GA integrated color vector quantization and curvelet transform

Yungang Zhang*, Tianwei Xu, Wei Gao

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

Abstract

Color and shape information have been two important image descriptors in Content Based Image Retrieval (CBIR) systems. The focus of this research is to find a method representing images with color and shape information in the way of human visual perception. The image retrieval approach proposed here depends on the color and shape features extracted by color Vector Quantization (VQ) and the Digital Curvelet Transform (DCT), respectively. The extracted color and shape features were combined and weighted by Genetic Algorithm (GA), then used for image similarity measurement. Experimental results show that the GA combined features can bring about improved image retrieval performance.

Original languageEnglish
Title of host publicationAdvances in Swarm Intelligence - Third International Conference, ICSI 2012, Proceedings
Pages406-413
Number of pages8
EditionPART 1
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event3rd International Conference on Swarm Intelligence, ICSI 2012 - Shenzhen, China
Duration: 17 Jun 201220 Jun 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume7331 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd International Conference on Swarm Intelligence, ICSI 2012
Country/TerritoryChina
CityShenzhen
Period17/06/1220/06/12

Keywords

  • color vector quantization
  • curvelet transform
  • genetic algorithm
  • Image retrieval

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