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Manifold Ranking for Sketch-Based 3D Model Retrieval

  • Lu Qian
  • Yachun FanEmail author
  • Mingquan Zhou
  • Hua Luan
  • Pu Ren
Chapter
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Keywords

Sketch retrieval 3D model Manifold ranking Visual vocabulary Line drawing 

Notes

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.

References

  1. 1.
    Li, B., Schreck, T., Godil, A., et al.: SHREC’12 Track: Sketch-based 3D shape retrieval. In: Eurographics Workshop on 3D Object Retrieval 2012 (3DOR 2012) (2012)Google Scholar
  2. 2.
    Funkhouser, T., Min, P., Kazhdan, M., et al.: A search engine for 3D models. ACM Trans. Graph. 22(1), 83–105 (2003)CrossRefGoogle Scholar
  3. 3.
    Yoon, S.M., Kuijper, A.: Sketch-based 3D model retrieval using compressive sensing classification. Electron. Lett. 47(21), 1181–1183 (2011)CrossRefGoogle Scholar
  4. 4.
    Li, B., Johan, H.: Sketch-based 3D model retrieval by incorporating 2D-3D alignment. Multimedia Tools Appl. 65(3), 363–385 (2013)CrossRefGoogle Scholar
  5. 5.
    Liu, Q.: A Survey of Recent View-based 3D Model Retrieval Methods (2012)Google Scholar
  6. 6.
    Saavedra, J.M., Bustos, B., Schreck, T., Yoon, S., Scherer, M.: Sketch-based 3D model retrieval using keyshapes for global and local representation. In: Proceedings of the 5th Eurographics Conference on 3D Object Retrieval, Cagliari, Italy, pp. 47–50. Eurographics Association (2012)Google Scholar
  7. 7.
    Wang, F., Lin, L., Tang, M.: A new sketch-based 3D model retrieval approach by using global and local features. Graph. Models 76, 128–139 (2014)CrossRefGoogle Scholar
  8. 8.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  9. 9.
    Bay, H., Ess, A., Tuytelaars, T., Van, G.L.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008)CrossRefGoogle Scholar
  10. 10.
    Ohbuchi, R., Furuya, T.: Scale-weighted dense bag of visual features for 3D model retrieval from a partial view 3D model. In: 2009 IEEE 12th International Conference on Computer Vision Workshops (ICCV Workshops), Kyoto, pp. 63–70 (2009)Google Scholar
  11. 11.
    Furuya, T., Ohbuchi, R.: Visual saliency weighting and cross-domain manifold ranking for sketch-based image retrieval. In: Gurrin, C., Hopfgartner, F., Hurst, W., Johansen, H., Lee, H., O’Connor, N. (eds.) MMM 2014. LNCS, vol. 8325, pp. 37–49. Springer, Heidelberg (2014). doi: 10.1007/978-3-319-04114-8_4 CrossRefGoogle Scholar
  12. 12.
    Eitz, M., Richter, R., Boubekeur, T., Hildebrand, K., Alexa, M.: Sketch-based shape retrieval. ACM Trans. Grap. 31(4) (2012)Google Scholar
  13. 13.
    He, J., Li, M., Tong, H., Zhang, C.: Manifold-ranking based image retrieval. In: Proceedings of MM 2004, pp. 9–16, 10–16 October 2004Google Scholar
  14. 14.
    Tong, H., He, J., Li, M., et al.: Manifold-ranking-based keyword propagation for image retrieval. EURASIP J. Appl. Signal Process. (2006). (79412SI)Google Scholar
  15. 15.
    Jin, H., He, R., Tao, W.: Multi-relationship based relevance feedback scheme in web image retrieval. Int. J. Innovative Comput. Inf. Control 4(6), 1315–1324 (2008)Google Scholar
  16. 16.
    Guan, Z., Bu, J., Mei, Q., Chen, C., Wang, C.: Personalized tag recommendation using graph-based ranking on multi-type interrelated objects, pp. 540–547 (2009)Google Scholar
  17. 17.
    Agarwal, S.: Ranking on graph data. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 25–32 (2006)Google Scholar
  18. 18.
    Guan, Z., Bu, J., Mei, Q., Chen, C., Wang, C.: Music recommendation by unified hypergraph: combining social media information and music content. In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 540–547 (2009)Google Scholar
  19. 19.
    Li, B., Li, Y., Li, C., et al.: SHREC’14 track: Extended large scale sketch-based 3D shape retrieval. In: Eurographics Workshop on 3D Object Retrieval, EG 3DOR (2014)Google Scholar
  20. 20.
    Furuya, T., Ohbuchi, R.: Similarity metric learning for sketch-based 3D object retrieval. Multimedia Tools Appl. 74(23), 10367–10392 (2014)CrossRefGoogle Scholar
  21. 21.
    Zhou, D.Y., Bousquet, O., Lal, T.N., Weston, J., Scholkopf, B.: Learning with local and global consistency. In: Advances in Neural Information Processing Systems, pp. 321–328 (2004)Google Scholar
  22. 22.
    Sivic, J., Zisserman, A.: Video Google: A text retrieval approach to object matching in videos, pp. 1470–1477 (2003)Google Scholar
  23. 23.
    Eitz, M., Hays, J., Alexa, M.: How do humans sketch objects? ACM Trans. Graph. 31(4) (2012)Google Scholar
  24. 24.
    Judd, T., Durand, F., Adelson, E.: Apparent ridges for line drawing. ACM Trans. Graph. 26(3), 19:1–19:8 (2007)CrossRefGoogle Scholar
  25. 25.
    Bosch, A., Zisserman, A., Munoz, X.: Representing shape with a spatial pyramid kernel. In: Proceedings of the 6th ACM International Conference on Image and Video Retrieval CIVR 2007, pp. 401–408. ACM, New York (2007)Google Scholar
  26. 26.
    Shilane, P., Min, P., Kazhdan, M., Funkhouser, T.: The Princeton shape benchmark, pp. 167–178 (2004)Google Scholar
  27. 27.
    Justin, Z., Moffat, A.: Inverted files for text search engines. ACM Comput. Surv. (CSUR) 38(2) (2006). Article No. 6Google Scholar
  28. 28.
    Wang, F., Kang, L., Li, Y.: Sketch-based 3D shape retrieval using convolutional neural networks. In: Computer Vision Foundation (2015)Google Scholar
  29. 29.
    Chinen, T.T., Reed, T.R.: A performance analysis of fast gabor transform methods. Graph. Models Image Process. 59(3), 117–127 (1997)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Lu Qian
    • 1
    • 2
  • Yachun Fan
    • 1
    • 2
    Email author
  • Mingquan Zhou
    • 1
    • 2
  • Hua Luan
    • 1
    • 2
  • Pu Ren
    • 1
    • 2
  1. 1.College of Information Science and TechnologyBeijing Normal UniversityBeijingChina
  2. 2.Key Laboratory of Digital Protection and Virtual Reality for Cultural HeritageBeijingChina

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