Shape and appearance context modeling

Wang, X., Doretto, G., Sebastian, T. B., Rittscher, J., and Tu, P. H.
Shape and appearance context modeling. In Proceedings of IEEE International Conference on Computer Vision, pp. 1–8, 2007.

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Abstract

In this work we develop appearance models for computing the similarity between image regions containing deformable objects of a given class in realtime. We introduce the concept of shape and appearance context. The main idea is to model the spatial distribution of the appearance relative to each of the object parts. Estimating the model entails computing occurrence matrices. We introduce a generalization of the integral image and integral histogram frameworks, and prove that it can be used to dramatically speed up occurrence computation. We demonstrate the abiity of this framework to recognize an individual walking across a network of cameras. Finally, we show that the proposed approach outperforms several other methods.

@INPROCEEDINGS{wangDSRT07iccv,
  author = {Wang, X. and Doretto, G. and Sebastian, T. B. and Rittscher, J. and
	Tu, P. H.},
  title = {Shape and appearance context modeling},
  booktitle = iccv,
  year = {2007},
  pages = {1--8},
  note = {\btohremove{\textsf{\textbf{AR: 23.5\%}}}},
  bib2html_pubtype = {Refereed Conferences},
  bib2html_rescat = {Video Surveillance, Appearance Modeling, Shape and Appearance Modeling,
	Integral Image Computations, Track Matching},
  abstract = {In this work we develop appearance models for computing the similarity
	between image regions containing deformable objects of a given class
	in realtime. We introduce the concept of shape and appearance context.
	The main idea is to model the spatial distribution of the appearance
	relative to each of the object parts. Estimating the model entails
	computing occurrence matrices. We introduce a generalization of the
	integral image and integral histogram frameworks, and prove that
	it can be used to dramatically speed up occurrence computation. We
	demonstrate the abiity of this framework to recognize an individual
	walking across a network of cameras. Finally, we show that the proposed
	approach outperforms several other methods.},
  file = {wangDSRT07iccv.pdf:doretto\\conference\\wangDSRT07iccv.pdf:PDF},
  owner = {doretto},
  pdf = {doretto\conference\wangDSRT07iccv.pdf},
  timestamp = {2007.01.19}
}

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