NDLI: Object detection in images: run-time complexity and parameter selection of support vector machines

Content Provider	IEEE Xplore Digital Library
Author	Ancona, N. Cicirelli, G. Stella, E. Distante, A.
Copyright Year	2002
Description	Author affiliation: Ist. Elaborazione Segnali ed Immagini, C.N.R, Bari, Italy (Ancona, N.; Cicirelli, G.; Stella, E.; Distante, A.)
Abstract	We address two aspects related to the exploitation of support vector machines (SVM) for classification in real application domains, such as the detection of objects in images. The first one concerns the reduction of the run-time complexity of a reference classifier without increasing its generalization error. We show that the complexity in test phase can be reduced by training SVM classifiers on a new set of features obtained by using principal component analysis (PCA). Moreover due to the small number of features involved, we explicitly map the new input space in the feature space induced by the adopted kernel function. Since the classifier is simply a hyperplane in the feature space, then the classification of a new pattern involves only the computation of a dot product between the normal to the hyperplane and the pattern. The second issue concerns the problem of parameter selection. In particular we show that the receiver operating characteristic curves, measured on a suitable validation set, are effective for selecting, among the classifiers the machine implements, the one having performances similar to the reference classifier. We address these two issues for the particular application of detecting goals during a football match.
Starting Page	426
Ending Page	429
File Size	342130
Page Count	4
File Format	PDF
ISBN	076951695X
ISSN	10514651
DOI	10.1109/ICPR.2002.1048330
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-08-11
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Object detection Runtime Support vector machines Support vector machine classification Cameras Machine learning Electronic mail Principal component analysis Kernel Particle measurements
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition

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