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Improving 3D Shape Retrieval Methods based on Bag-of-Feature Approach by using Local Codebooks

Abstract : Recent investigations illustrate that view-based methods, with pose normalization pre-processing get better performances in retrieving rigid models than other approaches and still the most popular and practical methods in the field of 3D shape retrieval. In this paper we present an improvement of 3D shape retrieval methods based on bag-of features approach. These methods use this approach to integrate a set of features extracted from 2D views of the 3D objects using the SIFT (Scale Invariant Feature Transform) algorithm into histograms using vector quantization which is based on a global visual codebook. In order to improve the retrieval performances, we propose to associate to each 3D object its local visual codebook instead of a unique global codebook. The experimental results obtained on the Princeton Shape Benchmark database, for the BF-SIFT method proposed by Ohbuchi, et al., and CM-BOF proposed by Zhouhui, et al., show that the proposed approach performs better than the original approach.
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Last modification on : Thursday, September 24, 2020 - 4:36:01 PM
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  • HAL Id : hal-00826558, version 1

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Dadi El Wardani, Daoudi El Mostafa, Claude Tadonki. Improving 3D Shape Retrieval Methods based on Bag-of-Feature Approach by using Local Codebooks. International Journal of future Generation Communication and Networking, 2012, Vol.5 (No 4), pp. 29-38. ⟨hal-00826558⟩

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