文件名称:Discriminativemodelsformulticlasobject
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- 图形图像处理(光照,映射..)
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- [PDF]
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- 2012-11-26
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- 9.51mb
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Many state-of-the-art approaches for object recognition
reduce the problem to a 0-1 classifi cation task. Such re-
ductions allow one to leverage sophisticated classifi ers for
learning. These models are typically trained independently
for each class using positive and negative examples cropped
from images. At test-time, various post-processing heuris-
tics such as non-maxima suppression (NMS) are required
to reconcile multiple detections within and between differ-
ent classes for each image. Though crucial to good perfor-
mance on benchmarks, this post-processing is usually de-
fi ned heuristically.-Many state-of-the-art approaches for object recognition reduce the problem to a 0-1 classification task. Such re-ductions allow one to leverage sophisticated classifiers for learning. These models are typically trained independently for each class using positive and negative examples cropped from images. At test-time, various post-processing heuris-tics such as non-maxima suppression (NMS) are required to reconcile multiple detections within and between differ-ent classes for each image. Though crucial to good perfor-mance on benchmarks, this post-processing is usually de-fined heuristically.
reduce the problem to a 0-1 classifi cation task. Such re-
ductions allow one to leverage sophisticated classifi ers for
learning. These models are typically trained independently
for each class using positive and negative examples cropped
from images. At test-time, various post-processing heuris-
tics such as non-maxima suppression (NMS) are required
to reconcile multiple detections within and between differ-
ent classes for each image. Though crucial to good perfor-
mance on benchmarks, this post-processing is usually de-
fi ned heuristically.-Many state-of-the-art approaches for object recognition reduce the problem to a 0-1 classification task. Such re-ductions allow one to leverage sophisticated classifiers for learning. These models are typically trained independently for each class using positive and negative examples cropped from images. At test-time, various post-processing heuris-tics such as non-maxima suppression (NMS) are required to reconcile multiple detections within and between differ-ent classes for each image. Though crucial to good perfor-mance on benchmarks, this post-processing is usually de-fined heuristically.
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Discriminative models for multi-class object.pdf