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Manifold Ranking for Sketch-Based 3D Model Retrieval

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Transactions on Edutainment XIII

Part of the book series: Lecture Notes in Computer Science ((TEDUTAIN,volume 10092))

Abstract

The demand for 3D model retrieval is increasing, and the sketch-based method has been proven to be the most effective and efficient approach to retrieve 3D models. The existing methods calculate distance based on feature extraction, showing its limitation in improving retrieval accuracy. Thus, a second ranking making use of relevance between features is a good way to go. In this paper, an extended manifold ranking method is presented as a new retrieval framework. Line drawings are abstracted to represent 3D models, and a visual vocabulary is used to describe the local features of both sketches and line drawings. To rank the similarities between models, a method of semantic classification as a constraint is presented. We use similarity weight to control the classification difference between models so that the ranking score of models that belong to the same class holds a higher similarity weight. Furthermore, based on the idea of manifold learning, a KNN algorithm is adopted to obtain better ranking results. Experiments on standard testing datasets have demonstrated that the proposed algorithm significantly improves the accuracy of 3D model retrieval and outperforms current state-of-the-art algorithms by comparison.

This project is supported by National Natural Science Foundation of China (61001168, 61402041); Fundamental Research Funds for the Central Universities of China under grant (2013YB67); Beijing Natural Science Foundation (4152028).

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Acknowledgments

The paper is based upon the work of the National Nature Science Foundation under Grants No. 61001168 and 61202198, the Fundamental Research Funds for the Central Universities (2013YB67), and Beijing Natural Science Foundation (4152028). We would like to thank the SHREC organizers for their valuable datasets.

Author information

Authors and Affiliations

  1. College of Information Science and Technology, Beijing Normal University, Beijing, China

    Lu Qian, Yachun Fan, Mingquan Zhou, Hua Luan & Pu Ren

  2. Key Laboratory of Digital Protection and Virtual Reality for Cultural Heritage, Beijing, China

    Lu Qian, Yachun Fan, Mingquan Zhou, Hua Luan & Pu Ren

Authors
  1. Lu Qian
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  2. Yachun Fan
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  3. Mingquan Zhou
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  4. Hua Luan
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  5. Pu Ren
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Corresponding author

Correspondence to Yachun Fan .

Editor information

Editors and Affiliations

  1. Hangzhou Normal University, Hangzhou, China

    Zhigeng Pan

  2. Graduate School of Media Design, Keio University, Yokohama-shi, Kanagawa, Japan

    Adrian David Cheok

  3. University of Education, Weingarten, Germany

    Wolfgang Müller

  4. Zhejiang University, Zhejiang, China

    Mingmin Zhang

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Qian, L., Fan, Y., Zhou, M., Luan, H., Ren, P. (2017). Manifold Ranking for Sketch-Based 3D Model Retrieval. In: Pan, Z., Cheok, A., Müller, W., Zhang, M. (eds) Transactions on Edutainment XIII. Lecture Notes in Computer Science(), vol 10092. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-54395-5_14

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  • DOI: https://doi.org/10.1007/978-3-662-54395-5_14

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  • Print ISBN: 978-3-662-54394-8

  • Online ISBN: 978-3-662-54395-5

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