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Unsupervised learning from a corpus for shape-based 3D model retrieval

Published: 26 October 2006 Publication History

Abstract

Arguably the most important issues in shape-based 3D model retrieval are methods to extract powerful yet compact shape features and methods to properly and promptly compare the shape features. In this paper, we explore a method to improve feature distance computation by employing unsupervised learning of the subspace of 3D shape features from a corpus. We employ an algorithm called Laplacian Eigenmaps proposed by Belkin, et al. to learn a manifold spanned by shape features of 3D models in the corpus. The learned manifold is approximated by an RBF network, onto which shape features are projected. Distances among shape features can then be computed effectively on the learned manifold. We combine this learning-based distance-computation method with a method to extract multiresolution shape features proposed by Ohbuchi, et al. Our experimental evaluation showed that the proposed method could significantly improve retrieval performance. Learning alone improved performance of two shape features we tried by about 5%. A combination of learning and multiresolution shape feature allowed about 10% gain in performance. As an example, the trained, multiresolution version of the SPRH gained 10% over the original single resolution, untrained SPRH shape feature.

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cover image ACM Conferences
MIR '06: Proceedings of the 8th ACM international workshop on Multimedia information retrieval
October 2006
344 pages
ISBN:1595934952
DOI:10.1145/1178677
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 26 October 2006

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Author Tags

  1. content-based retrieval
  2. manifold learning
  3. shape-based 3D model retrieval

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MM06
MM06: The 14th ACM International Conference on Multimedia 2006
October 26 - 27, 2006
California, Santa Barbara, USA

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  • (2016)A survey on indexing techniques for big dataKnowledge and Information Systems10.1007/s10115-015-0830-y46:2(241-284)Online publication date: 1-Feb-2016
  • (2014)3D shape retrieval and classification using multiple kernel learning on extended Reeb graphsThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-014-0926-530:11(1247-1259)Online publication date: 1-Nov-2014
  • (2013)Learning kernels on extended Reeb graphs for 3d shape classification and retrievalProceedings of the Sixth Eurographics Workshop on 3D Object Retrieval10.5555/2601307.2601312(25-32)Online publication date: 11-May-2013
  • (2013)A unified framework for multimodal retrievalPattern Recognition10.1016/j.patcog.2013.05.02346:12(3358-3370)Online publication date: 1-Dec-2013
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  • (2012)Search and Retrieval of Rich Media Objects Supporting Multiple Multimodal QueriesIEEE Transactions on Multimedia10.1109/TMM.2011.218134314:3(734-746)Online publication date: 1-Jun-2012
  • (2012)Investigating the Effects of Multiple Factors Towards More Accurate 3-D Object RetrievalIEEE Transactions on Multimedia10.1109/TMM.2011.217611114:2(374-388)Online publication date: 1-Apr-2012
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