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Exploring Discriminative Views for 3D Object Retrieval

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MultiMedia Modeling (MMM 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9516))

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Abstract

View-based 3D object retrieval techniques have become prevalent in various fields, and lots of ingenious studies have promoted the development of retrieval performance from different aspects. In this paper, we focus on the 2D projective views that represent the 3D objects and propose a boosting approach by evaluating the discriminative ability of each object’s views. Different from previous works on selecting representative views of query object, we investigate the discriminative information of each view in dataset. By employing the proposed reverse distance metric, we utilize the discriminative information for many to many view set matching. The proposed algorithm is then employed with various features to boost the multi-model graph learning method. We compare our approach with several state of the art methods on ETH-80 dataset and National Taiwan University 3D model dataset. The results demonstrate the effectiveness of our method and its excellent boosting performance.

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References

  1. Ansary, T.F., Daoudi, M., Vandeborre, J.P.: A bayesian 3-D search engine using adaptive views clustering. IEEE Trans. Multimedia 9(1), 78–88 (2007)

    Article  Google Scholar 

  2. Brigham, E.O., Brigham, E.: The fast Fourier transform and its applications, vol. 1. Prentice Hall Englewood Cliffs, NJ (1988)

    Google Scholar 

  3. Chen, D.Y., Tian, X.P., Shen, Y.T., Ouhyoung, M.: On visual similarity based 3D model retrieval. In: Computer Graphics Forum, vol. 22, pp. 223–232. Wiley Online Library (2003)

    Google Scholar 

  4. Cheng, J., Bian, W., Tao, D.: Locally regularized sliced inverse regression based 3D hand gesture recognition on a dance robot. Inf. Sci. 221, 274–283 (2013)

    Article  Google Scholar 

  5. Daras, P., Axenopoulos, A.: A 3D shape retrieval framework supporting multimodal queries. Int. J. Comput. Vis. 89(2–3), 229–247 (2010)

    Article  Google Scholar 

  6. Gao, Y., Dai, Q.: View-based 3-d object retrieval: challenges and approaches. IEEE Trans. Multimedia (2014)

    Google Scholar 

  7. Gao, Y., Dai, Q., Wang, M., Zhang, N.: 3D model retrieval using weighted bipartite graph matching. Sig. Process. Image Commun. 26(1), 39–47 (2011)

    Article  Google Scholar 

  8. Gao, Y., Dai, Q., Zhang, N.Y.: 3D model comparison using spatial structure circular descriptor. Pattern Recogn. 43(3), 1142–1151 (2010)

    Article  MATH  Google Scholar 

  9. Gao, Y., Tang, J., Hong, R., Yan, S., Dai, Q., Zhang, N., Chua, T.S.: Camera constraint-free view-based 3-D object retrieval. IEEE Trans. Image Process. 21(4), 2269–2281 (2012)

    Article  MathSciNet  Google Scholar 

  10. Gao, Y., Wang, M., Ji, R., Wu, X., Dai, Q.: 3-D object retrieval with hausdorff distance learning. IEEE Trans. Ind. Electron. 61(4), 2088–2098 (2014)

    Article  Google Scholar 

  11. Gao, Y., Wang, M., Tao, D., Ji, R., Dai, Q.: 3-D object retrieval and recognition with hypergraph analysis. IEEE Trans. Image Process. 21(9), 4290–4303 (2012)

    Article  MathSciNet  Google Scholar 

  12. Gao, Y., Wang, M., Zha, Z.J., Tian, Q., Dai, Q., Zhang, N.: Less is more: efficient 3-D object retrieval with query view selection. IEEE Trans. Multimedia 13(5), 1007–1018 (2011)

    Article  Google Scholar 

  13. Giorgi, D., Mortara, M., Spagnuolo, M.: 3D shape retrieval based on best view selection. In: Proceedings of the ACM Workshop on 3D Object Retrieval, pp. 9–14 (2010)

    Google Scholar 

  14. Hong, C., Yu, J., You, J., Chen, X., Tao, D.: Multi-view ensemble manifold regularization for 3D object recognition. Inf. Sci. (2015)

    Google Scholar 

  15. Khotanzad, A., Hong, Y.H.: Invariant image recognition by zernike moments. IEEE Trans. Pattern Anal. Mach. Intell. 12(5), 489–497 (1990)

    Article  Google Scholar 

  16. Laga, H.: Semantics-driven approach for automatic selection of best views of 3D shapes. In: Proceedings of the 3rd Eurographics Conference on 3D Object Retrieval, pp. 15–22 (2010)

    Google Scholar 

  17. Leibe, B., Schiele, B.: Analyzing appearance and contour based methods for object categorization. In: Computer Vision and Pattern Recognition, vol. 2, p. II-409 (2003)

    Google Scholar 

  18. Liu, Q., Yang, Y., Ji, R., Gao, Y., Yu, L.: Cross-view down/up-sampling method for multiview depth video coding. Sig. Process. Lett. 19(5), 295–298 (2012)

    Article  Google Scholar 

  19. Liu, Y., Wang, X.L., Wang, H.Y., Zha, H., Qin, H.: Learning robust similarity measures for 3D partial shape retrieval. Int. J. Comput. Vis. 89(2–3), 408–431 (2010)

    Article  Google Scholar 

  20. Ohbuchi, R., Osada, K., Furuya, T., Banno, T.: Salient local visual features for shape-based 3D model retrieval. In: IEEE International Conference on Shape Modeling and Applications, pp. 93–102 (2008)

    Google Scholar 

  21. Shih, J.L., Lee, C.H., Wang, J.T.: A new 3D model retrieval approach based on the elevation descriptor. Pattern Recogn. 40(1), 283–295 (2007)

    Article  MATH  Google Scholar 

  22. Wang, M., Hua, X.S., Hong, R., Tang, J., Qi, G.J., Song, Y.: Unified video annotation via multigraph learning. IEEE Trans. Circ. Syst. Video Technol. 19(5), 733–746 (2009)

    Article  Google Scholar 

  23. Yap, P.T., Paramesran, R., Ong, S.H.: Image analysis by Krawtchouk moments. IEEE Trans. Image Process. 12(11), 1367–1377 (2003)

    Article  MathSciNet  Google Scholar 

  24. Zhao, S., Chen, L., Yao, H., Zhang, Y., Sun, X.: Strategy for dynamic 3D depth data matching towards robust action retrieval. Neurocomputing 151, 533–543 (2015)

    Article  Google Scholar 

  25. Zhao, S., Yao, H., Yang, Y., Zhang, Y.: Affective image retrieval via multi-graph learning. In: ACM International Conference on Multimedia, pp. 1025–1028 (2014)

    Google Scholar 

  26. Zhao, S., Yao, H., Zhang, Y., Wang, Y., Liu, S.: View-based 3D object retrieval via multi-modal graph learning. Sig. Process. 112, 110–118 (2015)

    Article  Google Scholar 

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Acknowledgments

This work is supported by State Key Development Program of Basic Research of China (973 Program) (No. 2013CB733105), the National Natural Science Foundation of China (No. 61472103) and Key Program (No. 61133003).

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Authors and Affiliations

  1. State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, China

    Dong Wang, Bin Wang & Hong Liu

  2. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China

    Dong Wang, Sicheng Zhao & Hongxun Yao

Authors
  1. Dong Wang
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  2. Bin Wang
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  3. Sicheng Zhao
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  4. Hongxun Yao
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  5. Hong Liu
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Corresponding author

Correspondence to Bin Wang .

Editor information

Editors and Affiliations

  1. University of Texas at San Antonio, San Antonio, USA

    Qi Tian

  2. Dept. of Information Engineering, University of Trento, Povo, Trento, Italy

    Nicu Sebe

  3. EECS, University of Central Florida, Orlando, Florida, USA

    Guo-Jun Qi

  4. EURECOM, Sophia-Antipolis, France

    Benoit Huet

  5. Hefei University of Technology, Hefei, Anhui, China

    Richang Hong

  6. School of Computing and Information, Hefei University of Technology, Hefei, Anhui, China

    Xueliang Liu

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© 2016 Springer International Publishing Switzerland

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Wang, D., Wang, B., Zhao, S., Yao, H., Liu, H. (2016). Exploring Discriminative Views for 3D Object Retrieval. In: Tian, Q., Sebe, N., Qi, GJ., Huet, B., Hong, R., Liu, X. (eds) MultiMedia Modeling. MMM 2016. Lecture Notes in Computer Science(), vol 9516. Springer, Cham. https://doi.org/10.1007/978-3-319-27671-7_63

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  • DOI: https://doi.org/10.1007/978-3-319-27671-7_63

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27670-0

  • Online ISBN: 978-3-319-27671-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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