Content Based Image Clustering Technique Using Statistical Features and Genetic Algorithm

B. K. Alsaidi, B. J. Al-Khafaji, S. A. A. Wahab

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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References


N. Ghosh, S. Agrawal, M. Motwani, “A Survey of Feature Extraction for Content-Based Image Retrieval System”, Lecture Notes in Networks and Systems, Vol. 34, pp. 305-313, Springer, 2018

J. Wang, L. Wang, X. Liu, Y. Ren, Y. Yuan, “Color-Based Image Retrieval Using Proximity Space Theory”, Algorithms, Vol. 11, No. 8, ArticleID 115, 2018

M. Pham, G. Mercier, L. Bombru, “Color Texture Image Retrieval Based on Local Extrema Features and Riemannian Distance”, Journal of Imaging, Vol. 3, No. 4, ArticleID 43,2017

K. Kumar, Zain-ul-Abidin, J. P. Li, R. A. Shaikh, “Content Based Image Retrieval Using Gray Scale Weighted Average Method”, International Journal of Advanced Computer Science and Applications, Vol. 7, No. 1, pp. 1-6, 2016

E. M. F. El Houby, “Medical Images Retrieval using Clustering Technique”, International Journal on Recent and Innovation Trends in Computing and Communication, Vol. 3, No. 5, pp. 3134-3141, 2015

T. Peng, H. Gao, “A Cluster Priority Level Decision Method for Image Features”, International Journal of Database Theory and Application, Vol. 9, No. 2, pp. 171-182, 2016

A. Malakar, J. Mukherjee, “Image Clustering using Color Moments, Histogram, Edge and K-means Clustering”, International Journal of Science and Research, Vol. 2, No. 1, pp. 532-537, 2013

A. Obulesu, V. V. Kumar, L. Sumalatha, “Content based Image Retrieval Using Multi Motif Co-Occurrence Matrix”, International Journal of Image, Graphics and Signal Processing, Vol. 4, pp. 59-72, 2018

X. Zhang, J. Cui, W. Wang, C. Lin,”A Study for Texture Feature Extraction of High-Resolution Satellite Images Based on a Direction Measure and Gray Level Co-Occurrence Matrix Fusion Algorithm”, Sensors, Vol. 17, No. 7, ArticleID 1474, 2017

S. Soman, M. Ghorpade, V. Sonone, S. Chavan, “Content Based Image Retrieval Using Advanced Color and Texture Features”, International Conference in Computational Intelligence, Guangzhou, China, November 17-18, 2012

B. K. AlSaidi, “Automatic Approach for Word Sense Disambiguation Using Genetic Algorithms”, International Journal of Advanced Computer Science and Applications, Vol. 7, No. 1, pp. 41-44, 2016




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