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Similarity Learning for 3D Object Retrieval Using Relevance Feedback and Risk Minimization

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Abstract

We introduce a similarity learning scheme to improve the 3D object retrieval performance in a relevance feedback setting. The proposed algorithm relies on a score fusion approach that linearly combines elementary similarity scores originating from different shape descriptors into a final similarity function. Each elementary score is modeled in terms of the posterior probability of a database item being relevant to the user-provided query. The posterior parameters are learned via off-line discriminative training, while the optimal combination of weights to generate the final similarity function is obtained by on-line empirical ranking risk minimization. This joint use of on-line and off-line learning methods in relevance feedback not only improves the retrieval performance significantly as compared to the totally unsupervised case, but also outperforms the standard support vector machines based approach. Experiments on several 3D databases, including the Princeton Shape Benchmark, show also that the proposed algorithm has a better small sample behavior.

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References

  • Akgül, C. B. (2007). Density-based shape descriptors and similarity learning for 3D object retrieval. Ph.D. thesis, Dept. Signals-Images, Télécom ParisTech, Paris.

  • Akgül, C. B., Sankur, B., Yemez, Y., & Schmitt, F. (2008). Similarity score fusion by ranking risk minimization for 3D object retrieval. In Proceedings of the Eurographics workshop on 3D object retrieval.

  • Akgül, C. B., Sankur, B., Yemez, Y., & Schmitt, F. (2009). 3D model retrieval using probability density-based shape descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(6), 1117–1133.

    Article  Google Scholar 

  • Bustos, B., Keim, D. A., Saupe, D., Schreck, T., & Vranic, D. V. (2005). Feature-based similarity search in 3D object databases. ACM Computing Surveys, 37(4), 345–387.

    Article  Google Scholar 

  • Chang, C.-C., & Lin, C.-J. (2001). LIBSVM: a library for support vector machines. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.

  • Chen, Y., Zhou, X., & Huang, T. S. (2001). One-class SVM for learning in image retrieval. In Proceedings of the IEEE international conference on image processing (pp. 815–818).

  • Clémençon, S., Lugosi, G., & Vayatis, N. (2008). Ranking and empirical risk minimization of U-statistics. The Annals of Statistics, 36(2), 844–874.

    Article  MATH  MathSciNet  Google Scholar 

  • Datta, R., Joshi, D., Li, J., & Wang, J. Z. (2008). Image retrieval: ideas, influences, and trends of the new age. ACM Computing Surveys, 40(2), 1–60.

    Article  Google Scholar 

  • Giacinto, G., & Roli, F. (2004). Bayesian relevance feedback for content-based image retrieval. Pattern Recognition, 37(7), 1499–1508.

    Article  MATH  Google Scholar 

  • Giorgi, D., Biasotti, S., & Paraboschi, L. (2007). Shape retrieval contest 2007: watertight models track. In R. C. Veltkamp & F. B. T. Haar (Eds.), SHREC2007: 3D shape retrieval contest, Technical Report UU-CS-2007-015 (pp. 5–10).

  • Goodall, S., Lewis, P. H., Martinez, K., Sinclair, P. A. S., Giorgini, F., Addis, M., Boniface, M. J., Lahanier, C., & Stevenson, J. (2004). SCULPTEUR: multimedia retrieval for museums. In Proceedings of the international conference on image and video retrieval (pp. 638–646).

  • Guo, G., Jain, A. K., Ma, W., & Zhang, H. (2002). Learning similarity measure for natural image retrieval with relevance feedback. IEEE Transactions on Neural Networks, 12(4), 811–820.

    Google Scholar 

  • Hastie, T., Tibshirani, R., & Friedman, J. (2001). The elements of statistical learning: data mining, inference, and prediction. Berlin: Springer.

    MATH  Google Scholar 

  • Huang, J., Kumar, S. R., & Mitra, M. (1997). Combining supervised learning with color correlograms for content-based image retrieval. In Proceedings of the ACM international conference on multimedia (pp. 325–334). New York: ACM Press.

    Chapter  Google Scholar 

  • Ishikawa, Y., Subramanya, R., & Faloutsos, C. (1998). MindReader: query databases through multiple examples. In Proceedings of the international conference on very large databases (pp. 218–227).

  • Jayanti, S., Kalyanaraman, Y., Iyer, N., & Ramani, K. (2006). Developing an engineering shape benchmark for CAD models. Computer-Aided Design, 38(9), 939–953.

    Article  Google Scholar 

  • Laaksonen, J., Koskela, M., & Oja, E. (1999). PicSOM: self-organizing maps for content-based image retrieval. In Proceedings of the INNS-IEEE international joint conference on neural networks (pp. 1199–1207).

  • Leifman, G., Meir, R., & Tal, A. (2005). Semantic-oriented 3D shape retrieval using relevance feedback. The Visual Computer, 21(8), 865–875.

    Article  Google Scholar 

  • Lin, H.-T., Lin, C.-J., & Weng, R. C. (2007). A note on Platt’s probabilistic outputs for support vector machines. Machine Learning, 68(3), 267–276.

    Article  Google Scholar 

  • Nastar, C., Mitschke, M., & Meilhac, C. (1998). Efficient query refinement for image retrieval. In Proceedings of the IEEE international conference on computer vision and pattern recognition (pp. 547–552).

  • Novotni, M., Park, G.-J., Wessel, R., & Klein, R. (2005). Evaluation of kernel based methods for relevance feedback in 3D shape retrieval. In Proceedings of the international workshop on content-based multimedia indexing.

  • Papadakis, P., Pratitakis, I., & Theoharis, T. (2007). Efficient 3D shape matching and retrieval using a concrete radialized spherical projection representation. Pattern Recognition, 40(9), 2437–2452.

    Article  MATH  Google Scholar 

  • Peng, J., Bhanu, B., & Qing, S. (1999). Probabilistic feature relevance learning for content based image retrieval. Computer Vision Image Understanding, 75, 150–164.

    Article  Google Scholar 

  • Picard, R. W., Minka, T. P., & Szummer, M. (1999). Modeling user subjectivity in image libraries. In Proceedings of the IEEE international conference on image processing (pp. 777–780).

  • Platt, J. C. (1999). Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In A. Smola et al. (Eds.), Advances in large margin classifiers (pp. 61–74). Cambridge: MIT Press.

    Google Scholar 

  • Porkaew, K., Mehrotra, S., & Ortega, M. (1999). Query reformulation for content-based image retrieval in MARS. In Proceedings of the IEEE international conference on multimedia computing and systems (pp. 747–751).

  • Rocchio, J. J. (1966). Document retrieval system—optimization and evaluation. Ph.D. thesis, Harvard Computational Lab, Harvard University, Cambridge, MA.

  • Rui, Y., & Huang, T. S. (2000). Optimizing learning in image retrieval. In Proceedings of the IEEE international conference on computer vision and pattern recognition (pp. 236–243).

  • Rui, Y., Huang, T. S., Ortega, M., & Mehrotra, S. (1998). Relevance feedback: a power tool in interactive content-based image retrieval. IEEE Transactions on Circuits and Systems for Video Technology, 8(5), 644–655.

    Article  Google Scholar 

  • Santini, S., & Jain, R. (2000). Integrated browsing and querying for image database. IEEE Transactions on Multimedia, 7(3), 26–39.

    Article  Google Scholar 

  • Schettini, R., Ciocca, G., & Gagliardi, I. (1999). Content-based color image retrieval with relevance feedback. In Proceedings of the IEEE international conference on image processing (pp. 75–79).

  • Schölkopf, B., & Smola, A. (2002). Learning with kernels. Cambridge: MIT Press.

    Google Scholar 

  • Shilane, P., Min, P., Kazhdan, M., & Funkhouser, T. (2004). The Princeton shape benchmark. In Proceedings of the shape modeling international conference (pp. 167–178).

  • Smeulders, A. W. M., Worring, M., Santini, S., Gupta, A., & Jain, R. (2000). Content-based image retrieval at the end of the early years. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(12), 1349–1380.

    Article  Google Scholar 

  • Tangelder, J.-W. H., & Veltkamp, R.-C. (2008). A survey of content-based 3D shape retrieval methods. Multimedia Tools and Applications, 39, 441–471.

    Article  Google Scholar 

  • Tao, D., Tang, X., Li, X., & Wu, X. (2006). Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(7), 1088–1099.

    Article  Google Scholar 

  • Tieu, K., & Viola, P. (2000). Boosting image retrieval. In Proceedings of the IEEE international conference on computer vision and pattern recognition (pp. 228–235).

  • Tong, S., & Chang, E. (2001). Support vector machine active learning for image retrieval. In Proceedings of the ACM international conference on multimedia (pp. 107–118). New York: ACM Press.

    Google Scholar 

  • Vasconcelos, N., & Lippman, A. (1999). Learning from user feedback in image retrieval systems. In Proceedings of the neural information processing systems (Vol. 12).

  • Vasconcelos, N., & Lippman, A. (2000) A probabilistic architecture for content-based image retrieval. In Proceedings of the IEEE international conference on computer vision and pattern recognition (pp. 216–221).

  • Vranic, D. V. (2004). 3D model retrieval. Ph.D. thesis, University of Leipzig.

  • Wu, Y., Tian, Q., & Huang, T. S. (2000). Discriminant EM algorithm with application to image retrieval. In Proceedings of the IEEE international conference on computer vision and pattern recognition (pp. 222–227).

  • Zhang, L., Lin, F., & Zhang, B. (2001). Support vector machine learning for image retrieval. In Proceedings of the IEEE international conference on image processing (pp. 721–724).

  • Zhou, X. S., & Huang, T. S. (2001). Small sample learning during multimedia retrieval using BiasMap. In Proceedings of the IEEE international conference on computer vision and pattern recognition (pp. 11–17).

  • Zhou, X., & Huang, T. S. (2003). Relevance feedback for image retrieval: a comprehensive review. Multimedia Systems, 8(6), 536–544.

    Article  Google Scholar 

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Authors and Affiliations

  1. Video Processing and Analysis Group, Philips Research, Eindhoven, Netherlands

    Ceyhun Burak Akgül

  2. Electrical-Electronics Engineering Department, Boğaziçi University, Istanbul, Turkey

    Bülent Sankur

  3. Computer Engineering Department, Koç University, Istanbul, Turkey

    Yücel Yemez

  4. Department of Signal-Images, Télécom ParisTech, Paris, France

    Francis Schmitt

Authors
  1. Ceyhun Burak Akgül
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  2. Bülent Sankur
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  3. Yücel Yemez
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  4. Francis Schmitt
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Corresponding author

Correspondence to Ceyhun Burak Akgül.

Additional information

Part of this work has been done during C.B. Akgül’s Ph.D. studies at Boğaziçi University and Télécom ParisTech.

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Akgül, C.B., Sankur, B., Yemez, Y. et al. Similarity Learning for 3D Object Retrieval Using Relevance Feedback and Risk Minimization. Int J Comput Vis 89, 392–407 (2010). https://doi.org/10.1007/s11263-009-0294-1

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