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Non-rigid 3D Model Retrieval Based on Weighted Bags-of-Phrases and LDA

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Pattern Recognition (CCPR 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 662))

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Abstract

This paper presents an improved BOP model, called weighted bags-of-phrases (W-BOP), and its application in non-rigid 3D shape retrieval. The W-BOP model uses the Gaussian weighted function and the 3D points’ ring based neighborhood to construct the spatial arrangement model of visual words. Compared with BOP model, it can describe the 3D model more detailedly. Compared with the SS-BOW model and the BOFG model, it needn’t perform feature detection step and has higher computation efficiency. To further improve the retrieval performance, the LDA algorithm is used to reduce the dimension of the W-BOP descriptor. Extensive experiments have validated the effectiveness of the designed W-BOP model and LDA based non-rigid 3D model retrieval method.

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References

  1. Guo, Y., Bennamoun, M., Sohel, F., et al.: A comprehensive performance evaluation of 3D local feature descriptors. Int. J. Comput. Vis. 116, 1–24 (2015)

    MathSciNet  Google Scholar 

  2. López, G.L., Negrón, A.P.P., Jiménez, A.D.A., et al.: Comparative analysis of shape descriptors for 3D objects. Multimed. Tools Appl. 1–48 (2016)

    Google Scholar 

  3. Lu, K., He, N., Xue, J., et al.: Learning view-model joint relevance for 3D object retrieval. IEEE Trans. Image Process. 24(5), 1449–1459 (2015)

    Article  MathSciNet  Google Scholar 

  4. Guo, Y., Bennamoun, M., Sohel, F., et al.: 3D object recognition in cluttered scenes with local surface features: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 36(11), 2270–2287 (2014)

    Article  Google Scholar 

  5. Sun, J., Ovsjanikov, M., Guibas, L.: A concise and provably informative multi-scale signature based on heat diffusion. Comput. Graph. Forum 28(5), 1383–1392 (2009)

    Article  Google Scholar 

  6. Bronstein, M.M., Kokkinos, I.: Scale-invariant heat kernel signatures for non-rigid shape recognition. In: Proceedings of IEEE Conference on Computer Vision & Pattern Recognition, pp. 1704–1711 (2010)

    Google Scholar 

  7. Bronstein, A.M., Bronstein, M.M., Guibas, L.J., et al.: Shape google: geometric words and expressions for invariant shape retrieval. ACM Trans. Graph. 30(1), 623–636 (2011)

    Article  Google Scholar 

  8. Hou, T., Hou, X., Zhong, M., Qin, H.: Bag-of-feature-graphs: a new paradigm for non-rigid shape retrieval. In: International Conference on Pattern Recognition, pp. 1513–1516 (2012)

    Google Scholar 

  9. Zhang, L., Wang, C., Xiao, B., Shao, Y.: Image representation using bag-of-phrases. Acta Autom. Sinica 38(1), 46–54 (2012)

    Article  Google Scholar 

  10. Li, Y.: Non-rigid 3D model retrieval and tagging based on HKS. Master dissertation, Beijing Jiaotong University (2012)

    Google Scholar 

  11. Zeng, H., Zhang, R., Huang, M.: Improved 3D local feature descriptor based on rotational projection statistics and depth information. In: Zha, H., Chen, X., Wang, L., Miao, Q. (eds.) Communications in Computer and Information Science, vol. 547, pp. 139–147. Springer, Heidelberg (2015)

    Google Scholar 

  12. Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 711–720 (2013)

    Article  Google Scholar 

  13. Zhou, D., Yang, X., Peng, N.: A modified linear discriminant analysis and its application to face recognition. J. Shanghai Jiaotong Univ. 39(4), 527–530 (2005)

    Google Scholar 

  14. Zhuang, Z., et al.: Based on an optimized LDA algorithm for face recognition. J. Electron. Inf. Technol. 29(9), 2047–2049 (2007)

    Google Scholar 

  15. Siddiqi, K., Zhang, J., Macrini, D., Shokoufandeh, A., Bouix, S., Dickinson, S.: Retrieving articulated 3D models using medial surfaces. Mach. Vis. Appl. 19(4), 261–274 (2008)

    Article  MATH  Google Scholar 

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Acknowledgments

This article is supported by the National Natural Science Foundation of China (Grant No. 61375010 and No. 61005009).

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Authors and Affiliations

  1. School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, 100083, China

    Hui Zeng, Huijuan Wang & Siqi Li

  2. CAS Key Lab of Network Data Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China

    Wei Zeng

Authors
  1. Hui Zeng
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  2. Huijuan Wang
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  3. Siqi Li
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  4. Wei Zeng
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Corresponding author

Correspondence to Hui Zeng .

Editor information

Editors and Affiliations

  1. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  2. Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, China

    Xuelong Li

  3. Chinese Academy of Sciences, Institute of Computing Technology, Beijing, China

    Xilin Chen

  4. Tsinghua University , Beijing, China

    Jie Zhou

  5. Nanjing University of Science and Technology, Nanjing, China

    Jian Yang

  6. University of Electronic Science and Technology, Chengdu, Sichuan, China

    Hong Cheng

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© 2016 Springer Nature Singapore Pte Ltd.

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Zeng, H., Wang, H., Li, S., Zeng, W. (2016). Non-rigid 3D Model Retrieval Based on Weighted Bags-of-Phrases and LDA. In: Tan, T., Li, X., Chen, X., Zhou, J., Yang, J., Cheng, H. (eds) Pattern Recognition. CCPR 2016. Communications in Computer and Information Science, vol 662. Springer, Singapore. https://doi.org/10.1007/978-981-10-3002-4_38

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  • DOI: https://doi.org/10.1007/978-981-10-3002-4_38

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3001-7

  • Online ISBN: 978-981-10-3002-4

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