A Hybrid Approach for Shape Retrieval Using Genetic Algorithms and Approximate Distance

A Hybrid Approach for Shape Retrieval Using Genetic Algorithms and Approximate Distance

Saliha Mezzoudj (LaSTIC Laboratory, University Hadj Lakhdar, Batna, Algeria) and Kamal Eddine Melkemi (LMA Laboratory, University of Mohamed Khider, Biskra, Algeria)
Copyright: © 2018 |Pages: 17
DOI: 10.4018/IJCVIP.2018010105

Abstract

This article describes how the classical algorithm of shape context (SC) is still unable to capture the part structure of some complex shapes. To overcome this insufficiency, the authors propose a novel shape-based retrieval approach that is called HybMAS-GA using a multi-agent system (MAS) and a genetic algorithm (GA). They define a new distance called approximate distance (AD) to define a SC method by AD, which called approximate distance shape context (ADSC) descriptor. Furthermore, the authors' proposed HybMAS-GA is a star architecture where all shape context agents, N, are directly linked to a coordinator agent. Each retrieval agent must perform either a SC or an ADSC method to obtain a similar shape, started from its own initial configuration of sample points. This combination increases the efficiency of the proposed HybMAS-GA algorithm and ensures its convergence to an optimal images retrieval as it is shown through experimental results.
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The two major classes of methods to extract shape descriptors are the contour based approaches and the region based approaches. The shape descriptors of the first methods only extracted from contour information. However, the second methods extracted from all the pixels within a shape (Wang et al., 2012).

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