Color-frequency-orientation histogram based image retrieval

Zhuo Zhang, Xiaodong Gu, Sun-Yuan Kung

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

This paper proposes a multiclass image retrieval method using combined color-frequency-orientation histogram. Shape information, obtained via edge detector and Hough Transform, is also incorporated into the new feature. The feature has shown advantage in both unsupervised and supervised learning on Corel image dataset containing 10 categories of 1000 complex scenes. In unsupervised learning, comparing with histogram-based method [1], SIMPLIcity [2], FIRM [3], edge-based method [4], multi-resolution-based method [5], our approach respectively shows 25%, 14%, 10%, 7% and 2% improvement in accuracy. In supervised learning, we implement both one-against-one SVM and one-against-all SVM for multiclass classification. One-against-all SVM beats one-against-one SVM, achieving 95% accuracy with sufficient training.

Original languageEnglish (US)
Title of host publication2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings
Pages1321-1324
Number of pages4
DOIs
StatePublished - Oct 23 2012
Event2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Kyoto, Japan
Duration: Mar 25 2012Mar 30 2012

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012
CountryJapan
CityKyoto
Period3/25/123/30/12

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Keywords

  • Image classification
  • color-frequency-orientation histogram
  • image retrieval
  • support vector machines (SVMs)

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