CONTENT BASED IMAGE RETRIEVAL SYSTEM USING SVM CLASSIFICATION AND EVOLUTIONARY ALGORITHMS

The CBIR tends to index and retrieve images based on their visual content. CBIR avoids many problems associated with traditional ways of retrieving images by keywords. Thus, a growing interest in the area of CBIR has been established in recent years. The performance of a CBIR system mainly depends on the particular image representation and similarity matching function employed. The CBIR tends to index and retrieve images based on their visual content. CBIR avoids many problems associated with traditional ways of retrieving images by keywords. Thus, a growing interest in the area of CBIR has been established in recent years. The performance of a CBIR system mainly depends on the particular image representation and similarity matching function employed. So a new CBIR system is proposed which will provide accurate results as compared to previous developed systems. This introduces new composite framework for image classification in a content based image retrieval system. The proposed composite framework uses an evolutionary algorithm to select training samples for support vector machine (SVM). To design such a system, the most popular techniques of content based image retrieval are reviewed first. Our review reveals some limitations of the existing techniques, preventing them to accurately address some problems.