A factorized model for multiple SVM and multi-label classification for large scale multimedia indexing
Résumé
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.
Mots clés
- support vector machine
- multi-learner approach
- indexing
- image classification
- linear algebra
- multimedia databases
- support vector machines
- SVM-based large scale multimedia indexing
- multiple SVM
- Training
- Multimedia indexing and retrieval
- Predictive models
- Multimedia communication
- Kernel
- Euclidean distance
- optimized linear algebra routines
- multiple target concepts
- video retrieval
- multimedia retrieval
- multilabel classification
- large-scale dataset
- hashed structures
- factorized-MSVM
- factorized model
- computations factorization
- class imbalance
- TRECVid 2012 semantic indexing task
Domaines
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