Shape Retrieval through Angular Distance with Shortest Augmenting Path Algorithm

Chitra Chitra

doi:10.3844/jcssp.2011.1867.1874

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.

Highlights

The shape can be defined as the equivalence class world
This study shows that it direct use of gray values within the visible portion of recognizes simple objects with handwritten characters the objects instead of shapes
In a bulls eye test each image from a data base is given as the query and counts the number of correct images on top 40 matches

Summary

Introduction

Humans still out perform machines in most under a group of transformations. Our goal is to identifying the shapes when the similarities between design machines that can recognize the objects at levels the two shapes are high. The statistician’s definition of approaching or exceeding human performance. Shape matching can identify without finding the correspondence by using the intensity-based technique. An extensive survey of shape matching Belongie et al (2002) in a computer can broadly be classified into two approaches. They are brightness based and feature based methods. Serge Belongie et al (2002) presented a novel approach to measure similarity between shapes and object Brightness based method: Brightness or appearance.

Objectives

Results

Conclusion