@inproceedings{Shengyang2007,
abstract = {Component-based detection methods have demonstrated their promise by integrating a set of part-detectors to deal with large appearance variations of the target. However, an essential and critical issue, i.e., how to handle the imperfectness of part-detectors in the integration, is not well addressed in the literature. This paper proposes a detector ensemble model that consists of a set of substructure-detectors, each of which is composed of several part-detectors. Two important issues are studied both in theory and in practice, (1) finding an optimal detector ensemble, and (2) detecting targets based on an ensemble. Based on some theoretical analysis, a new model selection strategy is proposed to learn an optimal detector ensemble that has a minimum number of false positives and satisfies the design requirement on the capacity of tolerating missing parts. In addition, this paper also links ensemble-based detection to the inference in Markov random field, and shows that the target detection can be done by a max-product belief propagation algorithm. {\textcopyright} 2007 IEEE.},
author = {Dai, Shengyang and Yang, Ming and Wu, Ying and Katsaggelos, Aggelos},
booktitle = {2007 IEEE Conference on Computer Vision and Pattern Recognition},
doi = {10.1109/CVPR.2007.383274},
isbn = {1-4244-1179-3},
issn = {10636919},
month = {jun},
pages = {1--8},
publisher = {IEEE},
title = {{Detector Ensemble}},
url = {http://ieeexplore.ieee.org/document/4270299/},
year = {2007}
}

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