6533b7d8fe1ef96bd126ae8d
RESEARCH PRODUCT
Optimizing Query Perturbations to Enhance Shape Retrieval
Bilal MokhtariDominique MichelucciKamal Eddine MelkemiSebti Foufousubject
050101 languages & linguisticsComputer scienceInformationSystems_INFORMATIONSTORAGEANDRETRIEVALPopulationComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION02 engineering and technology[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]Search engineCompleteness (order theory)Genetic algorithm0202 electrical engineering electronic engineering information engineering0501 psychology and cognitive sciences[INFO]Computer Science [cs]educationMassively parallelComputingMilieux_MISCELLANEOUSThesaurus (information retrieval)education.field_of_studyCloning (programming)business.industry05 social sciencesPattern recognitionKey (cryptography)020201 artificial intelligence & image processingArtificial intelligencebusinessdescription
3D Shape retrieval algorithms use shape descriptors to identify shapes in a database that are the most similar to a given key shape, called the query. Many shape descriptors are known but none is perfect. Therefore, the common approach in building 3D Shape retrieval tools is to combine several descriptors with some fusion rule. This article proposes an orthogonal approach. The query is improved with a Genetic Algorithm. The latter makes evolve a population of perturbed copies of the query, called clones. The best clone is the closest to its closest shapes in the database, for a given shape descriptor. Experimental results show that improving the query also improves the precision and completeness of shape retrieval output. This article shows evidence for several shape descriptors. Moreover, the method is simple and massively parallel.
year | journal | country | edition | language |
---|---|---|---|---|
2020-03-18 |