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3D Shape Retrieval using Bag-of-feature method basing on 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 [9,10,11,12]. In this paper we present an improvement of the BF-SIFT method proposed by Ohbuchi et al [1]. This method is based on bag-of-features to integrate a set of features extracted from 2D views of the 3D objects using the SIFT (Scale Invariant Feature Transform [2]) algorithm into a histogram 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 [3] show that the proposed method performs better than the original method.
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https://hal-mines-paristech.archives-ouvertes.fr/hal-00753796
Contributor : Claire Medrala <>
Submitted on : Monday, November 19, 2012 - 4:13:52 PM
Last modification on : Thursday, September 24, 2020 - 4:36:01 PM

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Dadi El Wardani, Daoudi El Mostafa, Claude Tadonki. 3D Shape Retrieval using Bag-of-feature method basing on local codebooks. The 5th International Conference on Image and Signal Processing (ICISP 2012), Jun 2012, Agadir, Morocco. pp.391-396, ⟨10.1007/978-3-642-31254-0_44⟩. ⟨hal-00753796⟩

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