Abstract
Object detection has long been considered a binary-classification problem, but this formulation ignores the relationship between examples. Deformable part models, which achieve great success in object detction, have the same problem. We use learning to rank methods to train better deformable part models, and formulates the optimization problem as a generalized convex concave problem. Experiments show that, using same features and similar part configurations, performance of detection by the ranking model outperforms original deformable part models on both INRIA pedestrians and Pascal VOC benchmarks.
Chapter PDF
Similar content being viewed by others
References
Alexe, B., Deselaers, T., Ferrari, V.: What is an object? In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 73–80. IEEE (June 2010)
Azizpour, H., Laptev, I.: Object detection using strongly-supervised deformable part models. csc.kth.se (2012)
Blaschko, M.B., Vedaldi, A., Zisserman, A.: Simultaneous object detection and ranking with weak supervision. In: NIPS, vol. 1, p. 5 (2010)
Branson, S., Perona, P., Belongie, S.: Strong supervision from weak annotation: Interactive training of deformable part models. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 1832–1839 (November 2011)
Burges, C., Shaked, T., Renshaw, E., Lazier, A., Deeds, M., Hamilton, N., Hullender, G.: Learning to rank using gradient descent. In: Proceedings of the 22nd International Conference on Machine Learning, pp. 89–96. ACM (2005)
Burges, C.J., Ragno, R., Le. Learning, Q.V.: to rank with nonsmooth cost functions. In: NIPS, vol. 6, pp. 193–200 (2006)
Byrd, R.H., Lu, P., Nocedal, J., Zhu, C.: A limited memory algorithm for bound constrained optimization. SIAM Journal on Scientific Computing 16(5), 1190–1208 (1995)
Cheng, M.-M., Zhang, Z., Lin, W.-Y., Torr, P.H.S.: BING: Binarized normed gradients for objectness estimation at 300fps. In: IEEE CVPR (2014)
Dalal, N., Triggs, B., Europe, D.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 886–893. IEEE (2005)
Donmez, P., Svore, K.M., Burges, C.J.: On the local optimality of lambdarank. In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 460–467. ACM (2009)
Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes Challenge 2007 (VOC 2007) Results, http://www.pascal-network.org/challenges/VOC/voc2007/workshop/index.html
Felzenszwalb, P., Huttenlocher, D.: Distance transforms of sampled functions. Technical report, Cornell University (2004)
Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(9), 1627–1645 (2010)
Girshick, R.B., Felzenszwalb, P.F., McAllester, D.: Discriminatively trained deformable part models, release 5, http://people.cs.uchicago.edu/~rbg/latent-release5/
Joachims, T.: Optimizing search engines using clickthrough data. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 133–142. ACM (2002)
Dan, A.: Simovici and Chabane Djeraba. In: Mathematical Tools for Data Mining. Springer (2008)
van de Sande, K.E.A., Uijlings, J.R.R., Gevers, T., Smeulders, A.W.M.: Segmentation as selective search for object recognition. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 1879–1886. IEEE (2011)
Yuille, A.L., Rangarajan, A.: The concave-convex procedure (cccp). Advances in Neural Information Processing Systems 2, 1033–1040 (2002)
Zhu, L., Chen, Y., Yuille, A.: Learning a hierarchical deformable template for rapid deformable object parsing. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(6), 1029–1043 (2010)
Zhu, L., Chen, Y., Yuille, A., Freeman, W.: Latent hierarchical structural learning for object detection. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1062–1069. IEEE (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sun, C., Wang, X. (2014). A Ranking Part Model for Object Detection. In: Fränti, P., Brown, G., Loog, M., Escolano, F., Pelillo, M. (eds) Structural, Syntactic, and Statistical Pattern Recognition. S+SSPR 2014. Lecture Notes in Computer Science, vol 8621. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44415-3_42
Download citation
DOI: https://doi.org/10.1007/978-3-662-44415-3_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-44414-6
Online ISBN: 978-3-662-44415-3
eBook Packages: Computer ScienceComputer Science (R0)