Shape Based Image Retrieval Using Gradient Operators and Block Truncation Coding

Abstract

AbstractThe need of Content Based Image Retrieval (CBIR) arises because of digital era. It is very much required in the field of radiology to find the similar diagnostic images, in advertising to find the relevant stock, for cataloging in the field of geology, art and fashion. In CBIR, the set of image database is stored in terms of features where feature of an image can be calculated based on different criteria like shape, color, texture and spatial locations etc. Among three features shape is the prominent feature and helps to identify the image correctly. In this paper, we are proposing Shape Based Image Retrieval (SBIR) to retrieve shape features extracted using gradient operators and Block Truncation Coding (BTC). BTC improves the edge maps obtained using gradient masks like Robert, Sobel, Prewitt and Canny. The proposed image retrieval techniques are tested on generic image database with 1000 images spread across 10 categories. The average precision and recall of all queries are computed and considered for performance analysis. Among all the considered gradient operators for shape extraction “shape mask with BTC CBIR techniques” give better results. The performance ranking of the masks for proposed image retrieval methods can be listed as Canny (best performance), Prewitt, Sobel and lastly the Robert.KeywordsCBIRBTCShapeCannyPrewittSobelRobert