Shape Retrieval through Angular Distance with Shortest Augmenting Path Algorithm

Abstract

Problem statement: The shape of an object is very important in object recognition. Shape matching is a challenging problem, especially when articulation and deformation of a part occurs. These variations may be insignificant for human recognition but often cause a matching algorithm to give results that are inconsistent with our perception. Approach: We proposed a customized approach to measure similarity between shapes and exploit it for shape retrieval. The similarity was measured using the correspondence between the points on the two shapes and applying the aligning transformation. The correspondence was solved by the shape context with shortest augmenting path algorithm. Based on the correspondence, the aligning transformation is applied which best aligns the two shapes. Thin Plate Spline (TPS) with angular distance was to provide the better class of transformation maps. The matching error was calculated by the errors between the correspondence points on the two shapes and energy required in aligning transformation. Object recognition was achieved by the k-nearest neighbor algorithm. Result: The algorithm was efficient method for shape matching which performs the well on bulls eye test and produce 91.23% of retrieval rate on MPEG database. Conclusion: The proposed method is simple, invariant to noise and gives better error rate compare to the existing methods. It can also be extended to the handwritten characters, industrial objects, face recognition and COIL data base.