@inbook {6706,
	title = {A Hybrid Approach for Shape Retrieval Using Genetic Algorithms and Approximate Distance},
	booktitle = {Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms},
	year = {2021},
	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{\textquoteright} 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.

},
	url = {https://www.igi-global.com/chapter/a-hybrid-approach-for-shape-retrieval-using-genetic-algorithms-and-approximate-distance/271630},
	author = {Saliha Mezzoudj and Melkemi, Kamal-Eddine}
}
