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Multiple instance boosting for object detection (2006)
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| Venue: | In NIPS 18 |
| Citations: | 178 - 10 self |
BibTeX
@INPROCEEDINGS{Viola06multipleinstance,
author = {Paul Viola and John C. Platt and Cha Zhang},
title = {Multiple instance boosting for object detection},
booktitle = {In NIPS 18},
year = {2006},
pages = {1419--1426},
publisher = {MIT Press}
}
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Abstract
A good image object detection algorithm is accurate, fast, and does not require exact locations of objects in a training set. We can create such an object detector by taking the architecture of the Viola-Jones detector cascade and training it with a new variant of boosting that we call MIL-Boost. MILBoost uses cost functions from the Multiple Instance Learning literature combined with the AnyBoost framework. We adapt the feature selection criterion of MILBoost to optimize the performance of the Viola-Jones cascade. Experiments show that the detection rate is up to 1.6 times better using MILBoost. This increased detection rate shows the advantage of simultaneously learning the locations and scales of the objects in the training set along with the parameters of the classifier. 1
Keyphrases
multiple instance boosting object detection exact location good image object detection algorithm feature selection criterion anyboost framework viola-jones detector cascade viola-jones cascade new variant increased detection rate multiple instance learning literature cost function object detector detection rate
