Image classification is intrinsically a multiclass, nonlinear classification task. Support Vector Machines (SVMs) have been successfully exploited to tackle this problem, using one-vs-one or one-vs-all learning schemes to enable multiclass classification, and kernels designed for image classification to handle nonlinearities. To classify an image at test time, an SVM requires matching it against a small subset of the training data, namely, its support vectors (SVs). In the multiclass case, though, the union of the sets of SVs of each binary SVM may almost correspond to the full training set, potentially yielding an unacceptable computational complexity at test time. To overcome this limitation, in this work we propose a well-principled reduction method that approximates the discriminant function of a multiclass SVM by jointly optimizing the full set of SVs along with their coefficients. We show that our approach is capable of reducing computational complexity up to two orders of magnitude without significantly affecting recognition accuracy, by creating a super-sparse, budgeted set of virtual vectors.

Fast image classification with reduced multiclass support vector machines

ROLI, FABIO
2015

Abstract

Image classification is intrinsically a multiclass, nonlinear classification task. Support Vector Machines (SVMs) have been successfully exploited to tackle this problem, using one-vs-one or one-vs-all learning schemes to enable multiclass classification, and kernels designed for image classification to handle nonlinearities. To classify an image at test time, an SVM requires matching it against a small subset of the training data, namely, its support vectors (SVs). In the multiclass case, though, the union of the sets of SVs of each binary SVM may almost correspond to the full training set, potentially yielding an unacceptable computational complexity at test time. To overcome this limitation, in this work we propose a well-principled reduction method that approximates the discriminant function of a multiclass SVM by jointly optimizing the full set of SVs along with their coefficients. We show that our approach is capable of reducing computational complexity up to two orders of magnitude without significantly affecting recognition accuracy, by creating a super-sparse, budgeted set of virtual vectors.
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11567/1086042
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 3
  • ???jsp.display-item.citation.isi??? 2
social impact