NDLI: Classifier Optimization for Multimedia Semantic Concept Detection

Content Provider	IEEE Xplore Digital Library
Author	Sheng Gao Sun, Q.
Copyright Year	2006
Description	Author affiliation: Inst. for Infocomm. Res., Singapore (Sheng Gao; Sun, Q.)
Abstract	In this paper, we present an AUC (i.e., the area under the curve of receiver operating characteristics (ROC)) maximization based learning algorithm to design the classifier for maximizing the ranking performance. The proposed approach trains the classifier by directly maximizing an objective function approximating the empirical AUC metric. Then the gradient descent based method is applied to estimate the parameter set of the classifier. Two specific classifiers, i.e. LDF (linear discriminant function) and GMM (Gaussian mixture model), and their corresponding learning algorithms are detailed. We evaluate the proposed algorithms on the development set of TRECVID '05 for semantic concept detection task. We compare the ranking performances with other classifiers trained using the ML (maximum likelihood) or other error minimization methods such as SVM. The results of our proposed algorithm outperform ML and SVM on all concepts in terms of its significant improvements on the AUC or AP (average precision) values. We therefore argue that for semantic concept detection, where ranking performance is much interested than the classification error, the AUC maximization based classifiers are preferred
Starting Page	1489
Ending Page	1492
File Size	97730
Page Count	4
File Format	PDF
ISBN	1424403667
DOI	10.1109/ICME.2006.262824
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-07-09
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Support vector machine classification Algorithm design and analysis Information retrieval Design optimization Maximum likelihood detection Maximum likelihood estimation Minimization methods Area measurement Sun
Content Type	Text
Resource Type	Article

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