A novel efficient technique for extracting valid feature information

Sang Sung Park, Young Geun Shin, Dong Sik Jang

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

In this study, we proposed a quick and accurate algorithm for content-based image classification. The proposed method is also used to retrieve similar images from databases. In this paper color and texture information are used to represent image features. The basic idea is to extract color information about global and local features of images. A global color feature is extracted by an RGB model. While, a local color feature is extracted by an HSV model. In the case of a local feature, if it cannot be classified, the result is inaccurate retrieval. A GA (genetic algorithm) is used to extract local features which can be classified. Local features extracted by a GA are optimal representative features. In the experiment, the accuracy of image classification is measured using the proposed algorithm. Also, we compared the previous algorithm with the proposed algorithm in terms of image classification performance. As a result, the proposed algorithm showed higher performance in terms of accuracy.

Original languageEnglish
Pages (from-to)2654-2660
Number of pages7
JournalExpert Systems with Applications
Volume37
Issue number3
DOIs
Publication statusPublished - 2010 Mar 15

Fingerprint

Image classification
Color
Genetic algorithms
Textures
Experiments

Keywords

  • Feature information
  • Genetic algorithm
  • Image retrieval
  • Support vector machine

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Engineering(all)

Cite this

A novel efficient technique for extracting valid feature information. / Park, Sang Sung; Shin, Young Geun; Jang, Dong Sik.

In: Expert Systems with Applications, Vol. 37, No. 3, 15.03.2010, p. 2654-2660.

Research output: Contribution to journalArticle

Park, Sang Sung ; Shin, Young Geun ; Jang, Dong Sik. / A novel efficient technique for extracting valid feature information. In: Expert Systems with Applications. 2010 ; Vol. 37, No. 3. pp. 2654-2660.
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