NDLI: A factorized model for multiple SVM and multi-label classification for large scale multimedia indexing

Content Provider	IEEE Xplore Digital Library
Author	Safadi, B. Quenot, G.
Copyright Year	2015
Description	Author affiliation: LIG, Univ. Grenoble Alpes, Grenoble, France (Safadi, B.; Quenot, G.)
Abstract	This paper presents a set of improvements for SVM-based large scale multimedia indexing. The proposed method is particularly suited for the detection of many target concepts at once and for highly imbalanced classes (very infrequent concepts). The method is based on the use of multiple SVMs (MSVM) for dealing with the class imbalance and on some adaptations of this approach in order to allow for an efficient implementation using optimized linear algebra routines. The implementation also involves hashed structures allowing the factorization of computations between the multiple SVMs and the multiple target concepts, and is denoted as Factorized-MSVM. Experiments were conducted on a large-scale dataset, namely TRECVid 2012 semantic indexing task. Results show that the Factorized-MSVM performs as well as the original MSVM, but it is significantly much faster. Speed-ups by factors of several hundreds were obtained for the simultaneous classification of 346 concepts, when compared to the original MSVM implementation using the popular libSVM implementation.
Starting Page	1
Ending Page	6
File Size	268016
Page Count	6
File Format	PDF
ISBN	9781467368704
ISSN	19493991
DOI	10.1109/CBMI.2015.7153610
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-10
Publisher Place	Czech Republic
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Training class imbalance Multimedia indexing and retrieval support vector machine multi-learner approach Euclidean distance Predictive models Multimedia communication Kernel Indexing
Content Type	Text
Resource Type	Article

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