Active learning to recognize multiple types of plankton

Author

Luo, Tong ; Kramer, Kurt ; Samson, Scott ; Remsen, Andrew ; Goldgof, Dmitry B. ; Hall, Lawrence O. ; Hopkins, Thomas

Author_Institution

Dept. of Comput. Sci. & Eng., Univ. of South Florida, Tampa, FL, USA

Volume

3

fYear

2004

fDate

26-26 Aug. 2004

Firstpage

478

Abstract

Active learning has been applied with support vector machines to reduce the data labeling effort in pattern recognition domains. However, most of those applications only deal with two class problems. In this paper, we extend the active learning approach to multiple class support vector machines. The experimental results from a plankton recognition system indicate that our approach often requires significantly less labeled images to maintain the same accuracy level as random sampling.

Keywords

botany; learning (artificial intelligence); pattern recognition; random processes; sampling methods; support vector machines; active learning; multiple class support vector machines; pattern recognition; plankton recognition system; random sampling; Computer science; Image recognition; Image sampling; Machine learning; Marine vegetation; Pattern recognition; Sampling methods; Support vector machine classification; Support vector machines; Text categorization;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on

Conference_Location

Cambridge

ISSN

1051-4651

Print_ISBN

0-7695-2128-2

Type

conf

DOI

10.1109/ICPR.2004.1334570

Filename

1334570