Abstract
Support Vector Regression (SVR) has been a long standing problem in machine learning, and gains its popularity on various computer vision tasks. In this paper, we propose a structured support vector regression framework by extending the max-margin principle to incorporate spatial correlations among neighboring pixels. The objective function in our framework considers both label information and pairwise features, helping to achieve better cross-smoothing over neighboring nodes. With the bundle method, we effectively reduce the number of constraints and alleviate the adverse effect of outliers, leading to an efficient and robust learning algorithm. Moreover, we conduct a thorough analysis for the loss function used in structured regression, and provide a principled approach for defining proper loss functions and deriving the corresponding solvers to find the most violated constraint. We demonstrate that our method outperforms the state-of-the-art regression approaches on various testbeds of synthetic images and real-world scenes.
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McAuley, J.J., Caetano, T.S., Smola, A.J., Franz, M.O.: Learning high-order mrf priors of color images. In: International Conference on Machine Learning (2006)
Carr, P., Hartley, R.: Minimizing energy functions on 4-connected lattices using elimination. In: International Conference on Computer Vision (2009)
Szummer, M., Kohli, P., Hoiem, D.: Learning cRFs using graph cuts. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 582–595. Springer, Heidelberg (2008)
Anguelov, D., Taskar, B., Chatalbashev, V., Koller, D., Gupta, D., Heitz, G., Ng, A.: Discriminative learning of markov random fields for segmentation of 3d scan data. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2005)
Taskar, B., Chatalbashev, V., Koller, D.: Learning associative markov networks. In: International Conference on Machine Learning (2004)
Ionescu, C., Bo, L., Sminchisescu, C.: Structural svm for visual localization and continuous state estimation. In: International Conference on Computer Vision (2009)
Blaschko, M., Lampert, C.: Learning to localize objects with structured output regression. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 2–15. Springer, Heidelberg (2008)
Kim, M., Pavlovic, V.: Dimensionality reduction using covariance operator inverse regression. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2008)
Bo, L., Sminchisescu, C.: Structured output-associative regression. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2009)
Tsochantaridis, I., Joachims, T., Hofmann, T., Altun, Y.: Large margin methods for structured and interdependent output variables. Journal of Machine Learning Research 6, 1453–1484 (2005)
Taskar, B., Guestrin, C., Koller, D.: Max-margin markov networks. In: Advances in Neural Information Processing Systems, vol. 16. MIT Press, Cambridge (2004)
Weston, J., Schölkopf, B., Bousquet, O.: Joint kernel maps. In: Cabestany, J., Prieto, A.G., Sandoval, F. (eds.) IWANN 2005. LNCS, vol. 3512, pp. 176–191. Springer, Heidelberg (2005)
Teo, C., Smola, A., Vishwanathan, S., Le, Q.: A scalable modular convex solver for regularized risk minimization. In: International Conference on Knowledge Discovery and Data Mining (2007)
Pérez-Cruz, F., Camps-Valls, G., Soria-Olivas, E., Pérez-Ruixo, J.J., Figueiras-Vidal, A.R., Artés-Rodríguez, A.: Multi-dimensional function approximation and regression estimation. In: Dorronsoro, J.R. (ed.) ICANN 2002. LNCS, vol. 2415, p. 757. Springer, Heidelberg (2002)
Colliez, J., Dufrenois, F., Hamad, D.: Robust regression and outlier detection with svr: Application to optic flow estimation. In: British Machine Vision Conference (2006)
Vishwanathan, S.V.N., Schraudolph, N.N., Schmidt, M.W., Murphy, K.P.: Accelerated training of conditional random fields with stochastic gradient methods. In: International Conference on Machine Learning (2006)
Hirschmller, H., Scharstein, D.: Evaluation of cost functions for stereo matching. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2007)
Davies, E.: Laws’ texture energy in texture. In: Machine Vision: Theory, Algorithms, Practicalities, 2nd edn. Academic Press, San Diego (1997)
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Jia, K., Wang, L., Liu, N. (2011). Efficient Structured Support Vector Regression. In: Kimmel, R., Klette, R., Sugimoto, A. (eds) Computer Vision – ACCV 2010. ACCV 2010. Lecture Notes in Computer Science, vol 6494. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19318-7_46
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DOI: https://doi.org/10.1007/978-3-642-19318-7_46
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