Variants of dense descriptors and Zernike moments as features for accurate shape-based image retrieval

Abstract

Shape, being an important part of an object, has a special place in the field of shape-based image retrieval (SBIR). To retrieve most appropriate images, various descriptors are applied in SBIR like Zernike moments (ZMs), complex Zernike moments (CZMs) etc. Though ZMs/CZMs are good in SBIR but they are capable of extracting only global details of an image, hence something in addition to this is desirable to improve the performance of SBIR system. This paper presents experimental analysis of pixel-based dense descriptors such as local binary pattern (LBP), local directional pattern (LDP) and their variants. These descriptors are used as local features along with ZMs global features in achieving higher and accurate retrieval rate in SBIR system. We have analyzed these variants of LBP/LDP with various similarity measures on images. In case of ZMs, the magnitude component is used as global features. These methods are tested separately on suitable shape databases. Various databases used in the paper are MPEG-7 CE-2 region-based database, MPEG-7 CE-1 contour-based database and Trademark database. It can be concluded from the experimental analysis that the performance of LDP along with ZMs is better than that of ZMs alone and of ZMs along with other variants of LBP and LDP.