Two Tier Architecture for Content Based Image Retrieval Using Modified SVM and knn-GA - Lowanshi, Vinay; Shrivastava, Shweta (International Journal of Computer Sciences and Engineering, 2014)
Image retrieval is one of the most interesting and fastest growing research areas in the field of digital image processing as well as for the information retrieval from web contents. In most Content-Based Image Retrieval (CBIR) systems, an image is represented by a set of different level of visual features, by which can manage large databases. Most of the popular database removes the high-level semantic information.Here we this paper an novel approach named content based image retrieval using two tire architecture, to maintaining and reducing the exists gap between high-level and low-level features, where SVM classification is used in first layer after feature generation, therefore proceed it output as input into the second layer, where the resultant images again classified and will produce more accurate result while retrieval. And finally most similar images will retrieved according to the user specified query image.