DocumentCode :
1704374
Title :
Optimal supports for image matching
Author :
Lew, Michael S. ; Huang, Thomas S.
Author_Institution :
Dept. of Comput. Sci., Leiden Univ., Netherlands
fYear :
1996
Firstpage :
251
Lastpage :
254
Abstract :
The information theoretic approach provides a foundation for determining new insights and solutions toward image modeling and analysis problems. The underlying principle is that a search through an image can be viewed as a reduction of the expected uncertainty in the classification of the image. Specifically, we propose using the Kullback (1959) relative information for the determination of the support which maximizes the feature class separation, which consequently should minimize the probability of misclassifications. The methods are applied to face detection and two view image matching using internationally available databases
Keywords :
face recognition; feature extraction; image classification; image matching; information theory; Kullback relative information; databases; face detection; feature class separation; image analysis; image classification; image matching; image modeling; information theory; misclassification probability; optimal supports; uncertainty reduction; Computer science; Face detection; Image coding; Image matching; Information theory; Maximum likelihood detection; Maximum likelihood estimation; Mutual information; Pixel; Uncertainty;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Digital Signal Processing Workshop Proceedings, 1996., IEEE
Conference_Location :
Loen
Print_ISBN :
0-7803-3629-1
Type :
conf
DOI :
10.1109/DSPWS.1996.555508
Filename :
555508
Link To Document :
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