Pattern detection with information-based maximum discrimination and error bootstrapping

Author

Colmenarez, Antonio J. ; Huang, Thomas S.

Author_Institution

Dept. of Electr. & Comput. Eng., Illinois Univ., Urbana, IL, USA

Volume

1

fYear

1998

fDate

16-20 Aug 1998

Firstpage

222

Abstract

We have previously (1996, 1997) introduced a visual learning technique based on information-theoretic entropy. In that approach, positive and negative examples of a class of visual patterns were analyzed to obtain the probability model that best discriminate the class among others. Such models were tested in the context of maximum likelihood detection of faces and facial features. In this paper we further improve on that technique by using other family of probability model and by extending the optimization criteria to allow for error bootstrapping. The results include a detail analysis of the improvements obtained and a comparison of these pattern recognition algorithms

Keywords

entropy; face recognition; learning (artificial intelligence); maximum likelihood estimation; optimisation; probability; class discrimination; error bootstrapping; face detection; facial feature detection; information-based maximum discrimination; information-theoretic entropy; maximum likelihood detection; optimization criteria; pattern detection; pattern recognition algorithms; probability model; visual learning technique; visual patterns; Computer errors; Electrical capacitance tomography; Entropy; Face detection; Lighting; Markov processes; Maximum likelihood detection; Pattern analysis; Pattern recognition; System testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition, 1998. Proceedings. Fourteenth International Conference on

Conference_Location

Brisbane, Qld.

ISSN

1051-4651

Print_ISBN

0-8186-8512-3

Type

conf

DOI

10.1109/ICPR.1998.711121

Filename

711121