Clustering is an unsupervised classification of patterns into clusters (groups). Image clustering is a system of partitioning image data into clusters on the basis of similarities. It is used in many practical areas like Medical Diagnosis, Military, Remote sensing and etc. It is one type of image indexing where images are categorized into different groups based on their features, such as shape, color, or texture. The purpose of this paper is clustering of visually similar images from the image database using clustering algorithms. The proposed method uses the GLCM (Gray-Level Co-Occurrence Matrix) texture features. The extracted GLCM features are then clustered applying different clustering algorithms such as K-Means, K-Medoids and Improved K-Medoids partitioning clustering techniques. In this work, Corel-1k database is used. This work presents a comparative analysis of various clustering algorithms for image clustering with GLCM feature extraction technique. The experimental outcome of this work shows performance of different clustering algorithms.

Image Clustering, Feature Extraction, K-Means, K-Medoids

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