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• W2014767963 abstract "This paper addresses the problem of selecting features in a visual object detection setup where a detection algorithm is applied to an input image represented by a set of features. The set of features to be employed in the test stage is prepared in two training-stage steps. In the first step, a feature extraction algorithm produces a (possibly large) initial set of features. In the second step, on which this paper focuses, the initial set is reduced using a selection procedure. The proposed selection procedure is based on a novel evaluation function that measures the utility of individual features for a certain detection task. Owing to its design, the evaluation function can be seamlessly embedded into an AdaBoost selection framework. The developed selection procedure is integrated with state-of-the-art feature extraction and object detection methods. The presented system was tested on five challenging detection setups. In three of them, a fairly high detection accuracy was effected by as few as six features selected out of several hundred initial candidates." @default.
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• W2014767963 date "2008-08-01" @default.
• W2014767963 modified "2023-10-18" @default.
• W2014767963 title "Selecting features for object detection using an AdaBoost-compatible evaluation function" @default.
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• W2014767963 doi "https://doi.org/10.1016/j.patrec.2008.03.020" @default.
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