Robust shape similarity retrieval based on contour segmentation polygonal multiresolution and elastic matching

Abstract

In this paper, we are going to present a novel shape similarity retrieval algorithm that can be used to match and recognize 2D objects. The match process uses a new multi-resolution polygonal shape descriptor that is invariant to scale, rotation and translation. The shape descriptor equally segments the contour of any shape, regardless of its complexity, and captures three features around its center including the distance and slope relative to the center. All parameters are normalized relative to the max values. The novel shape matching algorithm uses the shape descriptor and applies it by linearly scanning a stored set of shapes and measuring the similarity using elastic comparisons of shape segments. Similarity measurement is achieved by the sum of differences distance measure. The multi-resolution segmentation provides flexibility for applications that have different time and space requirements while maintaining high accuracy results and the elastic matching adds an advantage when matching partially occluded shapes. We applied our algorithms on many test databases including the MPEG-7 shape core experiment and achieved the highest result reported with a score of 84.33% for the MPEG-7 Part B similarity test.