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ModelSeek: an effective 3D model retrieval system

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Abstract

Since object similarity is a subjective matter, the gap between low-level feature representations and high-level semantic concepts is a major problem in the field of content-based 3D model retrieval. This paper presents a novel composite model descriptor, which takes into account both visual and geometric characteristics of 3D models. It also proposes an original mapping mechanism from low-level model features to high-level semantic concepts based on the user’s retrieval history, and so this method belongs to long-term relevance feedback algorithms for 3D model retrieval. Finally, an effective 3D model retrieval system “ModelSeek” has been built, and implemented with the introduced model descriptor and mapping mechanism. The experimental results show that the approaches above not only have significantly improved the retrieval performance, but have also achieved better retrieval effectiveness than the state-of-the-art techniques on the publicly available 3D model criterion that Princeton Shape Benchmark (PSB) and several standard evaluation measures.

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References

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

    Article  Google Scholar 

  2. Atmosukarto I, Leow WK, Huang ZY (2005) Feature combination and relevance feedback for 3d model retrieval. In: Proceedings of the 11th international multimedia modelling conference, Melbourne, pp 334–339

  3. Bang HY, Chen T (2002) Feature space warping: an approach to relevance feedback. In: IEEE international conference on image processing, Rochester, pp 22–25

  4. Belkin M, Niyogi P (2003) Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput 15(6):1373–1396

    Article  MATH  Google Scholar 

  5. Borlund P (2003) The concept of relevance in ir. J Am Soc Inf Sci Technol 54(10):913–925

    Article  Google Scholar 

  6. Buckley C, Salton G (1995) Optimization of relevance feedback weights. In: Proceedings of the 18th annual international ACM SIGIR conference on research and development in information retrieval (SIGIR95), Seattle, pp 351–357

  7. Buckley C, Salton G, Allan J, Singhal A (1994) Automatic query expansion using smart: Trec 3. In: Proceedings of the third text retrieval conference (TREC-3), pp 69–80

  8. Burges CJC (1998) A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2(2):1–47

    Article  Google Scholar 

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

    Article  Google Scholar 

  10. Chen DY, Tian XP, Shen YT, Ouhyoung M (2003) On visual similarity based 3d model retrieval. Comput Graph Forum 22(3):223–232

    Article  Google Scholar 

  11. Cox IJ, Miller M, Minka TP, Papathomas T, Yianilos P (2000) The bayesian image retreival system, pichunter: theory, implementation, and psychophysical experiments. IEEE Trans Image Process 9(1):20–37

    Article  Google Scholar 

  12. Daras P, Zarpalas D, Tzovaras D, Strintzis MG (2006) Efficient 3d model search and retrieval using generalized 3d radon transforms. IEEE Trans Multimedia 8(1):101–114

    Article  Google Scholar 

  13. Djeraba C, Gabbouj M, Bouthemy P (2006) Multimedia indexing and retrieval: ever great challenges. Multimedia Tools and Applications 30(2):221–228

    Article  Google Scholar 

  14. Elad M, Tal A, Ar S (2001) Content based retrieval of vrml objects—an iterative and interactive approach. In: Proceedings of Eurographics workshop on multimedia, Manchester, pp 97–108

  15. Funkhouser T, Kazhdan M, Min P, Shilane P (2005) Shape-based retrieval and analysis of 3d models. Commun ACM 48(6):58–64

    Article  Google Scholar 

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

    Article  Google Scholar 

  17. Guetat G, Maitre M, Joly L, Lai SL, Tzumin L, Shinagawa Y (2006) Automatic 3-d grayscale volume matching and shape analysis. IEEE Trans Inf Technol Biomed 10(2):362–376

    Article  Google Scholar 

  18. Harter SP (1992) Psychological relevance and information science. J Am Soc Inf Sci 43(9):602–615

    Article  Google Scholar 

  19. He XF, King O, Ma WY (2003) Learning a semantic space from user’s relevance feedback for image retrieval. IEEE Trans Circuits Syst Video Technol 13(1):39–48

    Article  Google Scholar 

  20. Hilaga M, Shinagawa M, Kohmura T (2001) Topology matching for fully automatic similarity estimation of 3d shapes. In: Proceedings of the 28th annual conference on computer graphics (SIGGRAPH), Los Angeles, pp 203–212

  21. Iyer N, Jayanti S, Lou KY, Kalyanaraman Y, Ramani K (2005) Three-dimensional shape searching: state-of-the-art review and future trends. Computer-Aided Design 37(5):509–530

    Article  Google Scholar 

  22. Kolonias I, Tzovaras D, Malassiotis S, Strintzis MG (2005) Fast content-based search of vrml models based on shape descriptors. IEEE Trans Multimedia 7(1):114–126

    Article  Google Scholar 

  23. Leifman G, Meir R, Tal A (2005) Semantic-oriented 3d shape retrieval using relevance feedback. Vis Comput 21(10):865–875

    Article  Google Scholar 

  24. Leng B, Qin Z (2008) A powerful relevance feedback mechanism for content-based 3d model retrieval. Multimedia Tools and Applications 40(1):135–150

    Article  Google Scholar 

  25. Leng B, Qin Z, Li LQ (2007) Support vector machine active learning for 3d model retrieval. Journal of Zhengjiang University SCIENCE A 8(12):1953–1961

    Article  MATH  Google Scholar 

  26. Mandel MI, Poliner GE, Ellis DPW (2006) Support vector machine active learning for music retrieval. Multimedia Syst 12(1):3–13

    Article  Google Scholar 

  27. Novotni M, Park GJ, Wessel R, Klein R (2005) Evaluation of kernel based methods for relevance feedback in 3d shape retrieval. In: Proceedings the fourth international workshop on content-based multimedia indexing. Riga, Latvia

    Google Scholar 

  28. Osada R, Funkhouser T, Chazelle B, Dobkin D (2002) Shape distributions. ACM Trans Graph 21(4):807–832

    Article  Google Scholar 

  29. Papadakis P, Pratikakis I, Perantonis S, Theoharis T (2007) Efficient 3d shape matching and retrieval using a concrete radialized spherical projection representation. Pattern Recogn 40(9):2437–2452

    Article  MATH  Google Scholar 

  30. Pu JT, Ramani K (2006) On visual similarity based 2d drawing retrieval. Computer-Aided Design 38(3):249–259

    Article  Google Scholar 

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

    Article  Google Scholar 

  32. Roweis S, Saul L (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500):2323–2326

    Article  Google Scholar 

  33. Saracevic T (1975) A review of and a framework for the thinking on the notion in information science. J Am Soc Inf Sci 26(6):321–343

    Article  Google Scholar 

  34. Schamber L, Eisenberg M, Nilan MS (1990) A re-examination of relevance: toward a dynamic, situational definition. Inf Process Manag 26(6):755–776

    Article  Google Scholar 

  35. Sebastiani F (2002) Machine learning in automated text categorization. ACM Comput Surv 34(1):1–47

    Article  Google Scholar 

  36. Shih JL, Lee CH, Wang JT (2005) 3d object retrieval system based on grid d2. Electron Lett 41(4):179–181

    Article  Google Scholar 

  37. Shilane P, Min P, Kazhdan M, Funkhouser T (2004) The Princeton Shape Benchmark. In: Proceedings of shape modeling and applications, Palazzo Ducale, Genova, pp 167–178

  38. Swanson DR (1986) Subjective versus objective relevance in bibliographic retrieval systems. Libr Q 56(4):389–398

    Article  MathSciNet  Google Scholar 

  39. Tangelder JWH, Veltkamp RC (2008) A survey of content based 3d shape retrieval methods. Multimedia Tools and Applications 39(3):441–471

    Article  Google Scholar 

  40. Tenenbaum JB, Silva VD, Langford JC (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290(5500):2319–2323

    Article  Google Scholar 

  41. Tong S, Chang E (2001) Support vector machine active learning for image retrieval. In: Proceedings of the 9th annual ACM international conference on multimedia, Ottawa, pp 107–118

  42. Vranic DV (2003) An improvement of rotation invariant 3d shape descriptor based on functions on concentric spheres. In: IEEE international conference on image processing, Barcelona, pp 757–760

    Google Scholar 

  43. Vranic DV (2004) 3d model retrieval. PhD thesis, University of Leipzig, Leipzig

  44. Vranic DV (2005) Desire: a composite 3d-shape descriptor. In: Proceedings of IEEE international conference on multimedia and expo, Amsterdam, pp 962–965

  45. Yeh JS, Chen DY, Chen BY, Ouhyoung M (2005) A web-based three-dimensional protein retrieval system by matching visual similarity. Bioinformatics 21(13):3056–3057

    Article  Google Scholar 

  46. Zhou XS, Thomas SH (2003) Relevance feedback in image retrieval: a comprehensive review. Multimedia Syst 8(6):536–544

    Article  Google Scholar 

Download references

Acknowledgements

The 3D model database PSB is from the Shape Retrieval and Analysis Group at the University of Princeton, United States. This work is supported by the Key Projects in the National Science & Technology Pillar Program during the Eleventh Five-Year Plan Period (No. 2006BAB04A13), and the National Natural Science Foundation of China (No. 60803120).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Beihang University, Beijing, People’s Republic of China

    Biao Leng & Zhang Xiong

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  1. Biao Leng
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  2. Zhang Xiong
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Correspondence to Biao Leng.

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Leng, B., Xiong, Z. ModelSeek: an effective 3D model retrieval system. Multimed Tools Appl 51, 935–962 (2011). https://doi.org/10.1007/s11042-009-0424-3

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