Image Retrieval with Shape Features Extracted using Gradient Operators and Slope Magnitude Technique with BTC

Abstract

The paper discusses novel image retrieval methods based on shape features extracted using gradient operators and slope magnitude technique with Block Truncation Coding (BTC). Four variations of proposed „Mask-Shape-BTC‟ image retrieval techniques are proposed 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 11 categories. In all 55 queries (5 from each category) are fired on the image database. The average precision and recall of all queries are computed and considered for performance analysis. In all the considered gradient operators for shape extraction, „Mask-ShapeBTC‟ CBIR techniques outperform the „Mask-Shape‟ CBIR techniques. The performance ranking of the masks for proposed image retrieval methods can be listed as Robert (best performance), Prewitt, Sobel and lastly the Canny.