Color Image Clustering using Block Truncation Algorithm

TL;DR

This paper presents a method for clustering color images by extracting features using Block Truncation Coding and Color Moments, and then applying K-Means to group images effectively.

Contribution

It introduces a novel feature extraction approach combining BTC and Color Moments for improved image clustering.

Findings

Effective grouping of images based on color features

Enhanced clustering accuracy with proposed feature extraction

Demonstrated applicability to large image datasets

Abstract

With the advancement in image capturing device, the image data been generated at high volume. If images are analyzed properly, they can reveal useful information to the human users. Content based image retrieval address the problem of retrieving images relevant to the user needs from image databases on the basis of low-level visual features that can be derived from the images. Grouping images into meaningful categories to reveal useful information is a challenging and important problem. Clustering is a data mining technique to group a set of unsupervised data based on the conceptual clustering principal: maximizing the intraclass similarity and minimizing the interclass similarity. Proposed framework focuses on color as feature. Color Moment and Block Truncation Coding (BTC) are used to extract features for image dataset. Experimental study using K-Means clustering algorithm is conducted to group the image dataset into various clusters.