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Published Articles >> Table of Contents >> Abstract
2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '03) - Volume 1
p. 248
The Optimal Distance Measure for Object Detection
Shyjan Mahamud, Carnegie Mellon University
Martial Hebert, Carnegie Mellon University
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CVPR.2003.1211361
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| Abstract |
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We develop a multi-class object detection framework whose core component is a nearest neighbor search over object part classes. The performance of the overall system is critically dependent on the distance measure used in the nearest neighbor search. A distance measure that minimizes the mis-classification risk for the 1-nearest neighbor search can be shown to be the probability that a pair of input image measurements belong to different classes. In practice, we model the optimal distance measure using a linear logistic model that combines the discriminative powers of more elementary distance measures associated with a collection of simple to construct feature spaces like color, texture and local shape properties. Furthermore, in order to perform search over large training sets efficiently, the same framework was extended to find hamming distance measures associated with simple discriminators. By combining this discrete distance model with the continuous model, we obtain a hierarchical distance model that is both fast and accurate. Finally, the nearest neighbor search over object part classes was integrated into a whole object detection system and evaluated against an indoor detection task yielding good results.
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 Additional Information
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Citation:
Shyjan Mahamud, Martial Hebert,
"The Optimal Distance Measure for Object Detection,"
cvpr,
p. 248,
2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '03) - Volume 1,
2003
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