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.

• Type: Conference
• Year: 2004
• Where: ICIAR
• Authors: QingHua Wang, Luís Seabra Lopes, David M. J. Tax
• Keywords: Classification Methods, ICIAR 2004, One-class Classification Methods, Principal Component Analysis