Abstract
This paper presents a system of data decomposition and spatial mixture modeling for part based models. Recently, many enhanced part based models (with e.g., multiple features, more components or parts) have been proposed. Nevertheless, those enhanced models bring high computation cost together with the risk of over-fitting. To tackle this problem, we propose a data decomposition method for part based models which not only accelerates training and testing process but also improves the performance on average. Besides, the original part based model uses a strict rigid structural model to describe the distribution of each part location. It is not “deformable” enough, especially for those instances with different viewpoints or poses in the same aspect ratio. To address this problem, we present a novel spatial mixture modeling method. The spatial mixture embedded model is then integrated into the proposed data decomposition framework. We evaluate our system on the challenging PASCAL VOC2007 and PASCAL VOC2010 datasets, demonstrating the state-of-the-art performance compared with other related methods in terms of accuracy and efficiency.
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References
Felzenszwalb, P., Girshick, R., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. TPAMI 32, 1627–1645 (2010)
Schnitzspan, P., Roth, S., Schiele, B.: Automatic discovery of meaningful object parts with latent crfs. In: CVPR, pp. 121–128 (2010)
Zhang, J., Yu, Y., Huang, K., Tan, T.: Boosted Local Structured HOG-LBP for Object Localization. In: CVPR, pp. 1393–1400 (2011)
Zhu, L., Chen, Y., Yuille, A.L., Freeman, W.T.: Latent hierarchical structural learning for object detection. In: CVPR, pp. 1062–1069 (2010)
Yang, Y., Ramanan, D.: Articulated pose estimation with flexible mixtures-of-parts. In: CVPR, pp. 1385–1392
Schnitzspan, P., Fritz, M., Roth, S., Schiele, B.: Discriminative structure learning of hierarchical representations for object detection. In: CVPR, pp. 2238–2245 (2009)
Fischler, M., Elschlager, R.: The representation and matching of pictorial structures. IEEE Transactions on Computers C-22, 67–92 (1973)
Marr, D., Nishihara, H.K.: Representation and recognition of the spatial organization of three-dimensional shapes. In: Proceedings of the Royal Society of London. Series B, Biological Sciences, pp. 269–294 (1978)
Felzenszwalb, P.F., Huttenlocher, D.P.: Pictorial structures for object recognition. Int. J. Comput. Vision 61, 55–79 (2005)
Girshick, R., Felzenszwalb, P., McAllester, D.: Object Detection with Grammar Models. In: NIPS (2011)
Fergus, R., Perona, P., Zisserman, A.: Object class recognition by unsupervised scale-invariant learning. In: CVPR, pp. 264–271 (2003)
Wang, Y., Mori, G.: Hidden part models for human action recognition: Probabilistic versus max margin. TPAMI 33, 1310–1323 (2011)
Pandey, M., Lazebnik, S.: Scene recognition and weakly supervised object localization with deformable part-based models. In: ICCV, pp. 1307–1314 (2011)
Mark, E., Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The Pascal Visual Object Classes (VOC) Challenge. IJCV, 303–338
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR, pp. 886–893 (2005)
Ott, P., Everingham, M.: Shared parts for deformable part-based models. In: CVPR, pp. 1513–1520 (2011)
Hussain, S.U., Triggs, B.: Feature sets and dimensionality reduction for visual object detection, pp. 112.1–112.10. BMVA Press (2010)
Pedersoli, M., Vedaldi, A., Gonzalez, J.: A coarse-to-fine approach for fast deformable object detection. In: CVPR, pp. 1353–1360 (2011)
van de Sande, K.E.A., Uijlings, J.R.R., Gevers, T., Smeulders, A.W.M.: Segmentation as selective search for object recognition. In: ICCV, pp. 1879–1886 (2011)
Felzenszwalb, P.F., Girshick, R.B., Mcallester, D.: Cascade object detection with deformable part models. In: CVPR, pp. 2241–2248 (2010)
Zhang, J., Yu, Y., Zheng, S., Huang, K.: An empirical study of visual features for part based model. In: ACPR, pp. 219–223 (2011)
Felzenszwalb, P.F., Girshick, R.B., McAllester, D.: Discriminatively Trained Deformable Part Models, Release 4 (2010)
Desai, C., Ramanan, D., Fowlkes, C.: Discriminative models for multi-class object layout. In: ICCV, pp. 229–236 (2009)
Vedaldi, A., Gulshan, V., Varma, M., Zisserman, A.: Multiple kernels for object detection. In: ICCV, pp. 606–613 (2009)
Razavi, N., Gall, J., van Gool, L.: Scalable multi-class object detection. In: CVPR, pp. 1505–1512 (2011)
Divvala, S.K., Zitnick, C., Kapoor, A., Baker, S.: Detecting objects using unsupervised parts-based attributes. Technical Report CMU-RI-TR-11-10, Robotics Institute, Pittsburgh, PA (2010)
Schnitzspan, P., Fritz, M., Roth, S., Schiele, B.: Discriminative structure learning of hierarchical representations for object detection. In: CVPR, pp. 2238–2245 (2009)
Malisiewicz, T., Gupta, A., Efros, A.A.: Ensemble of exemplar-svms for object detection and beyond. In: ICCV, pp. 89–96 (2011)
ul Hussain, S.: Machine Learning Methods for Visual Object Detection. PhD thesis, University of Caen (2011)
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Zhang, J., Huang, Y., Huang, K., Wu, Z., Tan, T. (2013). Data Decomposition and Spatial Mixture Modeling for Part Based Model. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds) Computer Vision – ACCV 2012. ACCV 2012. Lecture Notes in Computer Science, vol 7724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37331-2_10
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DOI: https://doi.org/10.1007/978-3-642-37331-2_10
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