Skip to main content

Content-Based Object Movie Retrieval and Relevance Feedbacks


Object movie refers to a set of images captured from different perspectives around a 3D object. Object movie provides a good representation of a physical object because it can provide 3D interactive viewing effect, but does not require 3D model reconstruction. In this paper, we propose an efficient approach for content-based object movie retrieval. In order to retrieve the desired object movie from the database, we first map an object movie into the sampling of a manifold in the feature space. Two different layers of feature descriptors, dense and condensed, are designed to sample the manifold for representing object movies. Based on these descriptors, we define the dissimilarity measure between the query and the target in the object movie database. The query we considered can be either an entire object movie or simply a subset of views. We further design a relevance feedback approach to improving retrieved results. Finally, some experimental results are presented to show the efficacy of our approach.


  1. 1.

    Chen SE: QuickTime VR—an image-based approach to virtual environment navigation. Proceedings of the 22nd Annual ACM Conference on Computer Graphics and Interactive Techniques, August 1995, Los Angeles, Calif, USA 29–38.

    Google Scholar 

  2. 2.

    Hung Y-P, Chen C-S, Tsai Y-P, Lin S-W: Augmenting panoramas with object movies by generating novel views with disparity-based view morphing. Journal of Visualization and Computer Animation 2002,13(4):237-247. 10.1002/vis.292

    MATH  Article  Google Scholar 

  3. 3.

    Gortler SJ, Grzeszczuk R, Szeliski R, Cohen MF: The lumigraph. Proceedings of the 23rd Annual Conference on Computer Graphics (SIGGRAPH '96), August 1996, New Orleans, La, USA 43–54.

    Google Scholar 

  4. 4.

    Levoy M, Hanrahan P: Light field rendering. Proceedings of the 23rd Annual Conference on Computer Graphics (SIGGRAPH '96), August 1996, New Orleans, La, USA 31–42.

    Google Scholar 

  5. 5.

    McMillan L, Bishop G: Plenoptic modeling: an image-based rendering system. Proceedings of the 22nd Annual Conference on Computer Graphics (SIGGRAPH '95), August 1995, Los Angeles, Calif, USA 39–46.

    Google Scholar 

  6. 6.

    Zhang C, Chen T: A survey on image-based rendering—representation, sampling and compression. Signal Processing: Image Communication 2004,19(1):1-28. 10.1016/j.image.2003.07.001

    Google Scholar 

  7. 7.

    Castelli V, Bergman LD: Image Databases: Search and Retrieval of Digital Imagery. John Wiley & Sons, New York, NY, USA; 2002.

    Google Scholar 

  8. 8.

    Datta R, Li J, Wang JZ: Content-based image retrieval: approaches and trends of the new age. Proceedings of the 7th ACM SIGMM International Workshop on Multimedia Information Retrieval (MIR '05), November 2005, Singapore 253–262.

    Google Scholar 

  9. 9.

    Zhang R, Zhang Z, Li M, Ma W-Y, Zhang H-J: A probabilistic semantic model for image annotation and multi-modal image retrieval. Proceedings of the 10th IEEE International Conference on Computer Vision (ICCV '05), October 2005, Beijing, China 1: 846–851.

    Article  Google Scholar 

  10. 10.

    Chen D-Y, Tian X-P, Shen Y-T, Ouhyoung M: On visual similarity based 3D model retrieval. Computer Graphics Forum 2003,22(3):223-232. 10.1111/1467-8659.00669

    Article  Google Scholar 

  11. 11.

    Funkhouser T, Min P, Kazhdan M, et al.: A search engine for 3D models. ACM Transactions on Graphics 2003,22(1):83-105. 10.1145/588272.588279

    Article  Google Scholar 

  12. 12.

    Shilane P, Min P, Kazhdan M, Funkhouser T: The Princeton shape Benchmark. Proceedings of Shape Modeling International (SMI '04), June 2004, Genova, Italy 167–178.

    Google Scholar 

  13. 13.

    Zhang C, Chen T: An active learning framework for content-based information retrieval. IEEE Transactions on Multimedia 2002,4(2):260-268. 10.1109/TMM.2002.1017738

    Article  Google Scholar 

  14. 14.

    Atmosukarto I, Leow WK, Huang Z: Feature combination and relevance feedback for 3D model retrieval. Proceedings of the 11th International Multimedia Modelling Conference (MMM '05), January 2005, Melbourne, Australia 334–339.

    Chapter  Google Scholar 

  15. 15.

    Cyr CM, Kimia BB: 3D object recognition using shape similarity-based aspect graph. Proceedings of the 8th International Conference on Computer Vision (ICCV '01), July 2001, Vancouver, BC, USA 1: 254–261.

    Google Scholar 

  16. 16.

    Selinger A, Nelson RC: Appearance-based object recognition using multiple views. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '01), December 2001, Kauai, Hawaii, USA 1: 905–911.

    Google Scholar 

  17. 17.

    Mahmoudi S, Daoudi M: 3D models retrieval by using characteristic views. Proceedings of the 16th International Conference on Pattern Recognition (ICPR '02), August 2002, Quebec, Canada 2: 457–460.

    Article  Google Scholar 

  18. 18.

    Stricker MA, Orengo M: Similarity of color images. Storage and Retrieval for Image and Video Databases III, February 1995, San Jose, Calif, USA, Proceedings of SPIE 2420: 381–392.

    Article  Google Scholar 

  19. 19.

    Zhang DS, Lu G: A comparative study of Fourier descriptors for shape representation and retrieval. Proceedings of the 5th Asian Conference on Computer Vision (ACCV '02), January 2002, Melbourne, Australia 646–651.

    Google Scholar 

  20. 20.

    Khotanzad A, Hong YH: Invariant image recognition by Zernike moments. IEEE Transactions on Pattern Analysis and Machine Intelligence 1990,12(5):489-497. 10.1109/34.55109

    Article  Google Scholar 

  21. 21.

    Hse H, Newton AR: Sketched symbol recognition using Zernike moments. Proceedings of the 17th International Conference on Pattern Recognition (ICPR '04), August 2004, Cambridge, UK 1: 367–370.

    Article  Google Scholar 

  22. 22.

    Rui Y, Huang TS, Mehrotra S: Content-based image retrieval with relevance feedback in MARS. Proceedings of IEEE International Conference on Image Processing, October 1997, Santa Barbara, Calif, USA 2: 815–818.

    Article  Google Scholar 

  23. 23.

    Su Z, Zhang H, Li S, Ma S: Relevance feedback in content-based image retrieval: Bayesian framework, feature subspaces, and progressive learning. IEEE Transactions on Image Processing 2003,12(8):924-937. 10.1109/TIP.2003.815254

    Article  Google Scholar 

  24. 24.

    Zhou XS, Huang TS: Relevance feedback in image retrieval: a comprehensive review. Multimedia Systems 2003,8(6):536-544. 10.1007/s00530-002-0070-3

    Article  Google Scholar 

  25. 25.

    Cox IJ, Miller ML, Omohundro SM, Yianilos PN: PicHunter: Bayesian relevance feedback for image retrieval. Proceedings of the 13th International Conference on Pattern Recognition (ICPR '96), August 1996, Vienna, Austria 3: 361–369.

    Article  Google Scholar 

Download references

Author information



Corresponding author

Correspondence to Cheng-Chieh Chiang.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and Permissions

About this article

Cite this article

Chiang, CC., Chan, LW., Hung, YP. et al. Content-Based Object Movie Retrieval and Relevance Feedbacks. EURASIP J. Adv. Signal Process. 2007, 089691 (2007).

Download citation


  • Manifold
  • Feature Space
  • Quantum Information
  • Good Representation
  • Physical Object