An efficient image retrieval through DCT histogram quantization

Aamer Mohamed, F. Khellfi, Ying Weng, Jianmin Jiang, Stan Ipson

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

19 Citations (Scopus)

Abstract

This paper proposes a new simple method of Discrete Cosine Transform (DCT) feature extraction that is used to accelerate the speed and decrease the storage needed in the image retrieving process. Image features are accessed and extracted directly from JPEG compressed domain. This method extracts and constructs a feature vector of histogram quantization from partial DCT coefficient in order to count the number of coefficients that have the same DCT coefficient over all image blocks. The database image and query image is equally divided into a non overlapping 8×8 block pixel, each of which is associated with a feature vector of histogram quantization derived directly from discrete cosine transform DCT. Users can select any query as the main theme of the query image. The retrieved images are those from the database that bear close resemblance with the query image and the similarity is ranked according to the closest similar measures computed by the Euclidean distance. The experimental results are significant and promising and show that our approach can easily identify main objects while to some extent reducing the influence of background in the image and in this way improves the performance of image retrieval.)

Original languageEnglish
Title of host publication2009 International Conference on CyberWorlds, CW '09
Pages237-240
Number of pages4
DOIs
Publication statusPublished - 2009
Externally publishedYes
Event2009 International Conference on CyberWorlds, CW '09 - Bradford, United Kingdom
Duration: 7 Sep 200911 Sep 2009

Publication series

Name2009 International Conference on CyberWorlds, CW '09

Conference

Conference2009 International Conference on CyberWorlds, CW '09
Country/TerritoryUnited Kingdom
CityBradford
Period7/09/0911/09/09

Keywords

  • Content based image retrieval (CBIR)
  • DCT transform
  • Hisogram quantization)

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Graphics and Computer-Aided Design

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