Content based Image Retrieval using Clustering

Abstract

Content-based image retrieval (CBIR) systems aim to return the most relevant images in a database, according to the user’s opinion for a given query. Due to the dynamic nature of the problem, this may change the meaning of relevance among users for a same query. In this ID3 (Decision Tree) based support vector machine (SVM) method proposed to retrieve several features and shorten the semantic gap between low- level visual feature and high-level perception. The analysis of the proposed work is done using MATLAB 2009a simulator.