Object Detection with Improved Shape Context Features
Abstract
We presented a new approach for detecting objects by
combining top-down recognition with improved shape context
features and bottom-up segmentation.There are two main steps in this method:hypothesis generation step and verification step.In hypothesis generation step we use top-down recognition with improved shape context features and in verification step we use bottom-up segmentation. Our improved shape context features are more robust to
object deformation and background clutter. The shape context is used to generate a set of hypothesis of object location and figure ground mask, which have high recall and low precision rate.In the verification step, we first compute a set of feasible segmentation that are consistent with top-down object hypothesis, then we propose a false positive pruning procedure to prune out false positives. We found the fact that
false positive regions typically do not align with any feasible imagesegmentation.From our experiment we show that this simple framework achieve both high recall and high precision with only a few positive training examples and 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:
ECCV (2). (2002).
Levin, A., Weiss, Y.: Learning to combine bottom-up and top-down
segmentation. In: ECCV. (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: ECCV. (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 pair wise constraints between parts. In: ICCV. (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
contexts. Trans. Pattern Anal Mach. Intell. 27(11) (2005).
Thayananthan, A., Stenger, B., Torr, P.H.S., Cipolla, R.: Shape context
and chamfer 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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