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3D Shape Retrieval via Irrelevance Filtering and Similarity Ranking (IF/SR)

  • Xiaqing PanEmail author
  • Yueru Chen
  • C.-C. Jay Kuo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10116)

Abstract

A novel solution for the content-based 3D shape retrieval problem using an unsupervised clustering approach, which does not need any label information of 3D shapes, is presented in this work. The proposed shape retrieval system consists of two modules in cascade: the irrelevance filtering (IF) module and the similarity ranking (SR) module. The IF module attempts to cluster gallery shapes that are similar to each other by examining global and local features simultaneously. However, shapes that are close in the local feature space can be distant in the global feature space, and vice versa. To resolve this issue, we propose a joint cost function that strikes a balance between two distances. Irrelevant samples that are close in the local feature space but distant in the global feature space can be removed in this stage. The remaining gallery samples are ranked in the SR module using the local feature. The superior performance of the proposed IF/SR method is demonstrated by extensive experiments conducted on the popular SHREC12 dataset.

Keywords

Retrieval Performance Zernike Moment Random Forest Classifier Shape Retrieval Relevant Cluster 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Ming-Hsieh Department of Electrical EngineeringUniversity of Southern CaliforniaLos AngelesUSA

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