CONTENT BASED IMAGE RETRIEVAL USING SVM ALGORITHM AND CONTOURLET TRANSFORM COEFFICIENTS DISTRIBUTION.

Conventional content-based image retrieval schemes may suffer from practical applications. Image databases are often composed of several groups of images and span very different scales in the space of low-level visual descriptors the interactive retrieval of such image classes is then very difficult. To address this challenge, we propose the support vector machine (SVM) algorithm with contourlet transform coefficients distribution. SVM is used to find out the optimal result and to evaluate the generalization ability under the limited training samples. It gives faster result as compared to other. An SVM classifier can be learned from training data of relevance images and irrelevance images marked by users. Features of the face image are extracted in the spectrum domain using contourlet Transform and this transform addresses the problem of representing the images with smooth contours in different directions by providing two additional properties which are directionality and anisotropy. This method will overcome the introduction of the noisy examples by the users. In the proposed technique, multiple feature distances are combined to obtain image similarity. The extensive experiments are performed on two different image data bases to validate the superiority of the proposed method.