Image Feature Extraction Using CBIR, BTC and K-Means Clustering Algorithm
Vaibhav N. Dhage, Rajeshri R. Shelke · Advances in Computational Sciences and Technology · 2011
Mining Image data is one of the essential features in the present scenario. Image data is the major one which plays vital role in every aspect of the systems like business for marketing, hospital for surgery, engineering for construction, Web for publication and so on. 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. Content-based image retrieval (CBIR) systems utilize low level query image feature as identifying similarity between a query image and the image database. Color and texture both plays important image visual features used in Content-Based Image Retrieval to improve results. Grouping images into meaningful categories to display useful information is a challenging and important problem. In this paper, we will study the Contentbased image retrieval systems. Block Truncation Coding is used to extract features for image dataset and K-Means clustering algorithm is conducted to group the image dataset into various clusters.