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Sparse representation-based 3D model retrieval

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Abstract

In this study, we leveraged the sparse representation for multi-modal information fusion to handle 3D model retrieval problem. First, SIFT feature is extracted to represent the visual appearance of 2D view images for each 3D models. With this low-level feature representation, the Latent Dirichlet Allocation model is learned to generate the high-level & discriminative visual representation for individual 3D model. Then, we utilize the sparse representation framework to handle the key problem, the similarity measure between two different 3D models, for model retrieval. The performance of the proposed method is evaluated on the novel MV-RED 3D object dataset, which contains both RGB and depth 3D model data. The comparison experiments demonstrate the proposed sparse representation-based framework can benefit from multi-modal information fusion and consequently augment the performance.

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References

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  3. Bimbo AD, Pala P (2006) Content-based retrieval of 3d models. ACM Trans Multimed Comput Commun Appl (TOMM) 2(1):20–43

    Article  Google Scholar 

  4. Bustos B, Keim DA, Saupe D, Schreck T, Vranić DV (2005) Feature-based similarity search in 3d object databases. ACM Comput Surv (CSUR) 37(4):345–387

    Article  Google Scholar 

  5. Chen B, Shu H, Coatrieux G, Chen G, Sun X, Coatrieux J-L (2015) Color image analysis by quaternion-type moments. J Math Imaging Vision 51 (1):124–144

    Article  MathSciNet  MATH  Google Scholar 

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

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

    Article  Google Scholar 

  8. Funkhouser T, Min P, Kazhdan M, Chen J, Halderman A, Dobkin D, Jacobs D (2003) A search engine for 3d models. ACM Trans Graph (TOG) 22 (1):83–105

    Article  Google Scholar 

  9. Gao Y, Dai Q, Wang M, Zhang N (2011) 3d model retrieval using weighted bipartite graph matching. Sig Proc Image Comm 26(1):39–47

    Article  Google Scholar 

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

    Article  MATH  Google Scholar 

  11. Gao Y, Liu A, Nie W, Su Y, Dai Q, Chen F, Chen Y, Cheng Y, Dong S, Duan X et al (2015) 3d object retrieval with multimodal views

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

    Article  MathSciNet  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  14. Bin G, Sheng VS, Wang Z, Ho D, Osman S, Li S (2015) Incremental learning for ν-support vector regression. Neural Netw 67:140–150

    Article  Google Scholar 

  15. Guétat G, Maitre M, Joly L, Lai S-L, Lee T, Shinagawa Y (2006) Automatic 3-d grayscale volume matching and shape analysis. IEEE Trans Inf Technol Biomed 10(2):362–376

    Article  Google Scholar 

  16. Leng B, Xiong Z (2011) Modelseek: an effective 3d model retrieval system. Multimedia Tools Appl 51(3):935–962

    Article  Google Scholar 

  17. Li J, Li X, Yang B, Sun X (2015) Segmentation-based image copy-move forgery detection scheme. IEEE Trans Inf Forensics Secur 10(3):507–518

    Article  Google Scholar 

  18. Liu A-A, Nie W-Z, Su Y-T, Ma L, Hao T, Yang Z-X (2015) Coupled hidden conditional random fields for rgb-d human action recognition. Signal Process 112:74–82

    Article  Google Scholar 

  19. Liu A,Wang Z, NieW, Su Y (2015) Graph-based characteristic view set extraction and matching for 3D model retrieval. Inf Sci 320:429–442

  20. Nie W-Z, Liu A-A, Gao Z, Su Y-T (2015) Clique-graph matching by preserving global & local structure. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4503– 4510

  21. Nie W-Z, Liu A-A, Su Y-T (2015) 3d object retrieval based on sparse coding in weak supervision. J Vis Commun Image Represent

  22. Ohbuchi Ryutarou, Furuya Takahiko (2009) Scale-weighted dense bag of visual features for 3d model retrieval from a partial view 3d model. In: IEEE 12th international conference on computer vision workshops (ICCV Workshops), 2009. IEEE, pp 63–70

  23. Ohbuchi R, Osada K, Furuya T, Banno T (2008) Salient local visual features for shape-based 3d model retrieval. In: IEEE international conference on shape modeling and applications, 2008. SMI 2008. IEEE, pp 93–102

  24. Paquet E, Rioux M, Murching A, Naveen T, Tabatabai A (2000) Description of shape information for 2-d and 3-d objects. Signal Process Image Commun 16(1):103–122

    Article  Google Scholar 

  25. Regli WC, Cicirello VA (2000) Managing digital libraries for computer-aided design. Comput Aided Des 32(2):119–132

    Article  Google Scholar 

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

    Article  MATH  Google Scholar 

  27. Shilane P, Min P, Kazhdan M, Funkhouser T (2004) The princeton shape benchmark. In: Proceedings of shape modeling applications, 2004. IEEE, pp 167–178

  28. Sundar H, Silver D, Gagvani N, Dickinson S (2003) Skeleton based shape matching and retrieval. In: Shape modeling international, 2003. IEEE, pp 130–139

  29. Tangelder JWH, Veltkamp RC (2003) Polyhedral model retrieval using weighted point sets. Int J Image Graph 3(01):209–229

    Article  Google Scholar 

  30. Wang F, Li F, Dai Q, Er G (2008) View-based 3d object retrieval and recognition using tangent subspace analysis. In: Electronic imaging 2008. International Society for Optics and Photonics, pp 68220I– 68220I

  31. Wang X, Nie W (2015) 3d model retrieval with weighted locality-constrained group sparse coding. Neurocomputing 151:620–625

    Article  Google Scholar 

  32. Wen X, Shao L, Xue Y, Fang W (2015) A rapid learning algorithm for vehicle classification. Inf Sci 295:395–406

    Article  Google Scholar 

  33. Wong H-S, Ma B, Yu Z, Yeung PF, Ip HHS (2007) 3-d head model retrieval using a single face view query. IEEE Trans Multimedia 9(5):1026–1036

    Article  Google Scholar 

  34. Xia Z, Wang X, Zhang L, Qin Z, Sun X, Ren K (2016) A privacy-preserving and copy-deterrence content-based image retrieval scheme in cloud computing. IEEE Trans Inf Forensics Secur 11(11): 2594–2608

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China (61502337, 61472275, 61170239, 61303208), the Tianjin Research Program of Application Foundation and Advanced Technology (15JCYBJC162000), and the grant of Elite Scholar Program of Tianjin University (2014XRG-0046).

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  1. School of Electronics Information Engineering, Tianjin University, Tianjin, China

    Qun Cao, Yang An, Yingdi Shi & Xiaorong Zhu

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  1. Qun Cao
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  2. Yang An
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  3. Yingdi Shi
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  4. Xiaorong Zhu
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Correspondence to Yang An or Xiaorong Zhu.

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Cao, Q., An, Y., Shi, Y. et al. Sparse representation-based 3D model retrieval. Multimed Tools Appl 76, 20069–20079 (2017). https://doi.org/10.1007/s11042-016-4238-9

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