An Efficient Approach for Content based Image Retrieval using SVM, KNN-GA as Multilayer Classifier

Abstract

technology is growing day by day in various fields and image retrieval is one of the most of them, it is more interesting and fastest growing research areas. It is an effective and efficient tool for managing large image databases. In most Content-Based Image Retrieval (CBIR) systems, images are represented and differentiated by a set of low-level visual features; hence a direct correlation with high- level semantic information will be absent. Therefore, a gap exists between high-level information. In this paper they proposed novel approach for content based image retrieval was two tier architecture model is used for most accurate retrieval. In the first tier first feature extraction process done using PSO with SVM classifier, after successful classification in first tier the retrieved result has been passed into the second tier classifier. And in the second tier KNN classifier is used but as they knew that GA is one of the optimization technique and it produces the best optimized result in maximum cases so it is applied with the KNN classifier, and it produces more accurate and efficient compared result. Keywordsbased image retrieval (CBIR), feature extraction, SVM, PSO, KNN, GA, object optimization.