(Enter summary)
Abstract: We present an approach for learning to detect objects in still gray images, that is based on a sparse, part-based representation of objects. A vocabulary of information-rich object parts is automatically constructed from a set of sample images of the object class of interest. Images are then represented using parts from this vocabulary, along with spatial relations observed among them. Based on this representation, a feature-efficient learning algorithm is used to learn to detect instances of... (Update)
Context of citations to this paper: More
.... developed in the context of natural language problems is its success in facilitating learning in visual recognition problems [31, 1], as part of our attempt to study learning in multi modal environments. The knowledge representation and learning approach are identical...
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BibTeX entry: (Update)
S. Agarwal and D. Roth. Detecting objects by learning relations over parts. Technical Report UIUCDCSR - http://citeseer.ist.psu.edu/agarwal02detecting.html More
@inproceedings{ agarwalroth02,
author = "Shivani Agarwal and Dan Roth",
title = "Learning a sparse representation for object detection",
booktitle = "Proceedings of the 7th European Conference on Computer Vision",
volume = "4",
pages = "113-130",
year = "2002",
url = "citeseer.ist.psu.edu/agarwal02detecting.html" }
Citations (may not include all citations):
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Learning to resolve natural language ambiguities: A unied ap..
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A SNoW-based face detector
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Visual object recognition (context) - Logothetis, Sheinberg - 1996
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UIUC Computer Science Department (context) - Carlson, Cumby et al. - 1999
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Hierarchical structure in perceptual representation (context) - Palmer - 1977
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Recognition of objects and their component parts: responses .. (context) - Wachsmuth, Oram et al. - 1994
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