DocumentCode :
457217
Title :
A Probabilistic Approach to Fast and Robust Template Matching and its Application to Object Categorization
Author :
Mita, Takeshi ; Kaneko, Toshimitsu ; Hori, Osamu
Author_Institution :
Multimedia Lab., Toshiba Corp., Kawasaki
Volume :
2
fYear :
0
fDate :
0-0 0
Firstpage :
597
Lastpage :
601
Abstract :
This paper presents a new statistic, called probabilistic increment sign correlation (probabilistic ISC), for evaluating similarity between images of objects which have intra-class variation such as individual differences of human faces. The new statistic evaluates similarity between an input image and object classes, whereas most conventional methods, such as normalized cross-correlation, calculate correlation between an input image and a template. The new statistic is defined as a log-likelihood based on probabilities of observing the increment signs. Probabilistic ISC provides two advantages over conventional correlation-based methods: 1) robustness against the intra-class variation because it gives larger weights to stable features which are commonly observed in reference images and 2) robustness against noise and change in illumination. It yields higher performance even if a small number of reference images are given, whereas other methods such as the subspace method and AdaBoost cannot maintain their accuracy. We show these advantages through several experiments of face detection and face orientation estimation
Keywords :
image matching; probability; AdaBoost; face detection; face orientation estimation; fast template matching; intra-class variation; log-likelihood; object categorization; object image similarity evaluation; probabilistic increment sign correlation; robust template matching; Computational efficiency; Electronic mail; Face detection; Humans; Image processing; Laboratories; Lighting; Noise robustness; Probability; Statistics;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition, 2006. ICPR 2006. 18th International Conference on
Conference_Location :
Hong Kong
ISSN :
1051-4651
Print_ISBN :
0-7695-2521-0
Type :
conf
DOI :
10.1109/ICPR.2006.153
Filename :
1699276
Link To Document :
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