Content Based Image Clustering Technique Using Statistical Features and Genetic Algorithm

Authors

  • B. K. Alsaidi College of Administration and Economics, University of Baghdad, Iraq
  • B. J. Al-Khafaji Computer Science Department, College of Education for Pure Science/Ibn Al-Haitham, University of Baghdad, Iraq
  • S. A. A. Wahab Ministry of Education, Iraq
Volume: 9 | Issue: 2 | Pages: 3892-3895 | April 2019 | https://doi.org/10.48084/etasr.2497

Abstract

Text based-image clustering (TBIC) is an insufficient approach for clustering related web images. It is a challenging task to abstract the visual features of images with the support of textual information in a database. In content-based image clustering (CBIC), image data are clustered on the foundation of specific features like texture, colors, boundaries, shapes. In this paper, an effective CBIC) technique is presented, which uses texture and statistical features of the images. The statistical features or moments of colors (mean, skewness, standard deviation, kurtosis, and variance) are extracted from the images. These features are collected in a one dimension array, and then genetic algorithm (GA) is applied for image clustering. The extraction of features gave a high distinguishability and helped GA reach the solution more accurately and faster.

Keywords:

content based image clustering, statistical feature, genetic algorithm

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How to Cite

[1]
B. K. Alsaidi, B. J. Al-Khafaji, and S. A. A. Wahab, “Content Based Image Clustering Technique Using Statistical Features and Genetic Algorithm”, Eng. Technol. Appl. Sci. Res., vol. 9, no. 2, pp. 3892–3895, Apr. 2019.

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