Abstract
We develop an object detection method combining top-down recognition with bottom-up image segmentation. There are two main steps in this method: a hypothesis generation step and a verification step. In the top-down hypothesis generation step, we design an improved Shape Context feature, which is more robust to object deformation and background clutter. The improved Shape Context is used to generate a set of hypotheses of object locations and figure-ground masks, which have high recall and low precision rate. In the verification step, we first compute a set of feasible segmentations that are consistent with top-down object hypotheses, then we propose a False Positive Pruning(FPP) procedure to prune out false positives. We exploit the fact that false positive regions typically do not align with any feasible image segmentation. Experiments show that this simple framework is capable of achieving both high recall and high precision with only a few positive training examples and that this method can be generalized to many object classes.
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Viola, P.A., Jones, M.J.: Rapid object detection using a boosted cascade of simple features. In: CVPR (2001)
Borenstein, E., Ullman, S.: Class-specific, top-down segmentation. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2351, Springer, Heidelberg (2002)
Levin, A., Weiss, Y.: Learning to combine bottom-up and top-down segmentation. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, Springer, Heidelberg (2006)
Leibe, B., Seemann, E., Schiele, B.: Pedestrian detection in crowded scenes. In: CVPR (2005)
Ferrari, V., Tuytelaars, T., Gool, L.J.V.: Object detection by contour segment networks. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, Springer, Heidelberg (2006)
Kokkinos, I., Maragos, P., Yuille, A.L.: Bottom-up & top-down object detection using primal sketch features and graphical models. In: CVPR (2006)
Zhao, L., Davis, L.S.: Closely coupled object detection and segmentation. In: ICCV (2005)
Ren, X., Berg, A.C., Malik, J.: Recovering human body configurations using pairwise constraints between parts. In: Sebe, N., Lew, M.S., Huang, T.S. (eds.) Computer Vision in Human-Computer Interaction. LNCS, vol. 3766, Springer, Heidelberg (2005)
Mori, G., Ren, X., Efros, A.A., Malik, J.: Recovering human body configurations: Combining segmentation and recognition. In: CVPR (2004)
Srinivasan, P., Shi, J.: Bottom-up recognition and parsing of the human body. In: CVPR (2007)
Felzenszwalb, P.F., Huttenlocher, D.P.: Pictorial structures for object recognition. International Journal of Computer Vision 61(1) (2005)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR (2005)
Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Trans. Pattern Anal. Mach. Intell. 24(4) (2002)
Mori, G., Belongie, S.J., Malik, J.: Efficient shape matching using shape contexts. IEEE Trans. Pattern Anal. Mach. Intell 27(11) (2005)
Thayananthan, A., Stenger, B., Torr, P.H.S., Cipolla, R.: Shape context and chamfer matching in cluttered scenes. In: CVPR (2003)
Rubner, Y., Tomasi, C., Guibas, L.J.: A metric for distributions with applications to image databases. In: ICCV (1998)
Ramanan, D.: Using segmentation to verify object hypotheses. In: CVPR (2007)
Shi, J., Malik, J.: Normalized cuts and image segmentation. In: CVPR (1997)
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Wang, L., Shi, J., Song, G., Shen, If. (2007). Object Detection Combining Recognition and Segmentation. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds) Computer Vision – ACCV 2007. ACCV 2007. Lecture Notes in Computer Science, vol 4843. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76386-4_17
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DOI: https://doi.org/10.1007/978-3-540-76386-4_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76385-7
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