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QPert: Query Perturbation to improve shape retrieval algorithms

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Abstract

Although there is a wide range of shape descriptors available in the literature, most of them are restricted to a specific class of shapes and no one can achieve satisfactory shape retrieval results when used with different classes of shapes. Introducing new descriptors, improving, or merging existing descriptors are potential strategies for enhancing shape retrieval algorithms. In this paper, we propose a Query Perturbation-based (QPert) method for shape retrieval. QPert perturbs the query shape to create copies or clones that are closer than the query itself to the database shapes. Clones are created by adding a small noise to the coordinates of a randomly selected subset of mesh vertices or applying genetic operators between existing clones. A Genetic Algorithm (GA) gradually develops a population of clones so that the fittest clones get closer and closer to their most similar shapes in the database. The GA is implemented as a multiagent system (MAS) that enables any number of shape descriptors, classical or modern, to cooperate without the need for synchronization or direct communication between agents. Experimental results and comparisons demonstrate the advantages of this approach, regardless of the shape descriptors used.

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Notes

  1. ReMESH is Copyright(C) 2004-2011:IMATI-GE / CNR

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Authors and Affiliations

  1. Département d’informatique, Université de Batna 2, 53, Route de Constantine, Fésdis, Batna, 05078, Algérie

    Abdelhakim Benkrama & Kamal Eddine Melkemi

  2. Computer Science Department, Biskra University, BP 145 RP, Biskra, 07000, Algeria

    Bilal Mokhtari

  3. Computer Science Department, University of Sharjah, Sharjah, UAE

    Sebti Foufou

  4. ICB UMR 6303, Université de Bourgogne, Dijon, 21000, France

    Sebti Foufou

  5. Esi, Ecole Supérieure d’Informatique, Alger, 16309, Algérie

    Omar Boudraa

  6. LIB, Laboratoire d’Informatique de Bourgogne, Université de Bourgogne, Dijon, 21000, France

    Dominique Michelucci

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  1. Abdelhakim Benkrama
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  3. Kamal Eddine Melkemi
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  4. Sebti Foufou
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Correspondence to Bilal Mokhtari.

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Highlights

•   QPert is a Query Perturbation-based (QPert) method for shape retrieval and shape matching.

•   QPert is implemented as multiagent architecture and exhibits three types of agents Distance agents, Retrieval agents, and a Coordinator agent.

•   QPert creates clones of the query shape, then computes distances between the clones and shapes in the database.

•   Clones are improved with genetic algorithms. The best clone is the closest to shapes in the database.

•   QPert uses both classical and modern (i.e., machine learning-based shape) descriptors and significantly improves their performances.

•   QPert is extensible as adding a new shape descriptor is straightforward, and the more shape descriptors are used, the better.

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Benkrama, A., Mokhtari, B., Melkemi, K.E. et al. QPert: Query Perturbation to improve shape retrieval algorithms. Multimed Tools Appl 83, 25461–25485 (2024). https://doi.org/10.1007/s11042-023-16376-9

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