Abstract
This paper addresses the problem of local histogram-based image feature selection for learning binary classifiers. We show a novel technique which efficiently combines histogram feature projection with the conditional mutual information (CMI) based classifier selection scheme. Moreover, we investigate cost-sensitive modifications of the CMI-based selection procedure, which further improves the classification performance. Extensive evaluations show that the proposed methods are suitable for object detection and recognition tasks.
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Utasi, Á. (2012). Entropic Selection of Histogram Features for Efficient Classification. In: Gimel’farb, G., et al. Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2012. Lecture Notes in Computer Science, vol 7626. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34166-3_29
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DOI: https://doi.org/10.1007/978-3-642-34166-3_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34165-6
Online ISBN: 978-3-642-34166-3
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