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ICIAR
2004
Springer

Visual Object Recognition Through One-Class Learning

9 years 18 days ago
Visual Object Recognition Through One-Class Learning
Abstract. In this paper, several one-class classification methods are investigated in pixel space and PCA (Principal component Analysis) subspace having in mind the need of finding suitable learning and classification methods to support natural language grounding in the context of Human-Robot Interaction. Face and non-face classification is used as an example to demonstrate effectiveness of these one-class classifiers. The idea is to train target class models with only target (face) patterns, but still keeping good discrimination over outlier (never seen non-target) patterns. Some discussion is given and promising results are reported.
QingHua Wang, Luís Seabra Lopes, David M. J
Added 01 Jul 2010
Updated 01 Jul 2010
Type Conference
Year 2004
Where ICIAR
Authors QingHua Wang, Luís Seabra Lopes, David M. J. Tax
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