Query by image content using biased discriminant euclidean algorithm and clustering techniques

Abstract

Query by image content is a method to retrieve the most important images from the image database. It is an answer for the problem of searching for digital images in large database. A large number of relevance feedback schemes have been developed to improve the performance of content based image retrieval. In this paper we propose biased discriminant Euclidean embedding that form intraclass geometry and interclass discrimination. In this method images can be grouped by their similarity. In order to achieve this, firstly the visual features of the images are found out. In addition to this we use two clustering algorithms to assemble the images into clusters using their low level visual features. Here we have to filter the images in the hierarchical clustering and then apply the clustered images to K-Means, so that we can get the most relevant images. Query image is compared directly with the images in the clusters. Therefore number of comparisons is reduced because query image is compared only with the images in the clusters instead of comparing with all images in the database.