Titolo:  Fast image classification with reduced multiclass support vector machines
Autori: 
Data di pubblicazione:  2015
Autori:  Melis, Marco; Piras, Luca; Biggio, Battista; Giacinto, Giorgio; Fumera, Giorgio; Roli, Fabio
Presenza coautori internazionali:  no
Lingua:  Inglese
Titolo del libro:  Image Analysis and Processing – ICIAP 2015 (Part 2)
ISBN:  978-3-319-23233-1
978-3-319-23234-8
Editore:  Springer
Tutti i curatori:  Vittorio Murino, Enrico Puppo
Serie:  LECTURE NOTES IN COMPUTER SCIENCE
Volume:  9280
Pagina iniziale:  78
Pagina finale:  88
Numero di pagine:  11
Digital Object Identifier (DOI):  http://dx.doi.org/10.1007/978-3-319-23234-8_8
Codice identificativo Scopus:  2-s2.0-84944724979
Codice identificativo ISI:  WOS:000364991400008
Revisione (peer review):  Esperti anonimi
Nome del convegno:  18th International Conference on Image Analysis and Processing, ICIAP 2015
Periodo del convegno:  September 7-11, 2015
Luogo del convegno:  Genoa, Italy
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.
Tipologia: 4.1 Contributo in Atti di convegno

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