Maximal disk based histogram for shape retrieval

Abstract

In this paper, we propose a robust and efficient representation scheme for shape retrieval, which is based on the normalized maximal disks used to represent the shape of an object. The maximal disks are extracted by means of a fast skeletonization technique with a pruning algorithm. The logarithm of the radii of the normalized maximal disks is used to construct a histogram to represent the shape. The retrieval performance of this maximal disk based histogram approach is compared to other methods, including moment invariants, Zernike moments, and curvature scale-space. Experimental results show that our proposed representation scheme outperforms the other methods under affine transformation and different noise levels.