Content Based Image Retrieval System using SVM Technique

Multimedia applications are increasing rapidly and a large number of digital images are stored in database. For the effective retrieval of the desired images from a huge image database study of a content-based image retrieval (CBIR) technique has become an important research issue. In this proposed work, image retrieval is done through color and texture feature extraction. For feature extraction different algorithms are used like color auto correlogram, HSV (Hue Saturation Value) histogram and color moments. Also, texture features like mean square energy, mean amplitude of 2D wavelet component and standard deviation of wavelet coefficients. Features of query image and database images are classified and compared using support vector machine and similarity measures. Features are compared based on pair wise euclidean distance between query image and database image by various methods such as L1, cityblock, minkowski, chebychev, cosine, correlation and spearman.