A Content-based Image Retrieval System Based on Polar Raster Edge Sampling Signature

Abstract

Content Based Image Retrieval (CBIR) is used to effectively retrieve required images from fairly large databases. CBIR extracts images that are relevant to the given query image, based on the features extracted from the contents of the image. Most of the CBIR systems available in the literature are not rotation and scale invariant. Retrieval efficiency is also poor. In this paper, shape features are extracted from the database images and the same are polar raster scanned into specified intervals in both radius and angle, using the proposed Polar Raster Edge Sampling Signature (PRESS) algorithm. Counts of edge points lying in these bins are stored in the feature library. When a query image passed on to the system, the features are extracted in the similar fashion. Subse- quently, similarity measure is performed between the query image features and the data- base image features based on Euclidian Distance similarity measure and the database images that are relevant to the given query image are retrieved. PRESS algorithm has been successfully implemented and tested in a CBIR System developed by us. This technique pre- serves rotation and scale invariance. It is evaluated by querying different images. The retrieval efficiency is also evaluated by determining precision-recall values for the retrieval results.