Hierarchical complexity measures for effective shape-based image retrieval

Abstract

A novel descriptor, called HCMD, which is based on hierarchical complexity measures, is proposed for generic shape based image retrieval. HCMD belongs to region-based methods and iteratively partitions a shape into smaller blocks along various directions. The geometrical properties of these smaller blocks, which are derived from each iterative cut, are measured to form a hierarchical description for the shape. The descriptor has the ability to characterize a shape from coarse to fine, and can effectively capture its complex inner structural features. Shape matching based on HCMD is independent of the rotation, scaling, translation, and mirror transform of the shape. It has low computational complexity and can effectively handle both the contour and region shapes. Three standard test sets, namely, the MPEG-7 CE-1 contour shape database, MPEG-7 CE-2 region shape database, and COIL-20 database, are used to evaluate the performance of the proposed HCMD, and extensive comparisons with state-of-the-art approaches, including five region-based descriptors, four point-set based descriptors, and two curve-based descriptors, are conducted. All experimental results indicate that the proposed HCMD outperforms these approaches in terms of their comprehensive performance based on the retrieval rates, retrieval efficiency, and general applications.