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http://dx.doi.org/10.5392/JKCA.2010.10.12.084

Improving Weak Classifiers by Using Discriminant Function in Selecting Threshold Values  

Shyam, Adhikari (전북대학교 전자정보공학부)
Yoo, Hyeon-Joong (상명대학교 정보통신공학과)
Kim, Hyong-Suk (전북대학교 전자정보공학부)
Publication Information
The Journal of the Korea Contents Association / v.10, no.12, 2010 , pp. 84-90 More about this Journal
Abstract
In this paper, we propose a quadratic discriminant analysis based approach for improving the discriminating strength of weak classifiers based on simple Haar-like features that were used in the Viola-Jones object detection framework. Viola and Jones built a strong classifier using a boosted ensemble of weak classifiers. However, their single threshold (or decision boundary) based weak classifier is sub-optimal and too weak for efficient discrimination between object class and background. A quadratic discriminant analysis based approach is presented which leads to hyper-quadric boundary between the object class and background class, thus realizing multiple thresholds based weak classifiers. Experiments carried out for car detection using 1000 positive and 3000 negative images for training, and 500 positive and 500 negative images for testing show that our method yields higher classification performance with fewer classifiers than single threshold based weak classifiers.
Keywords
Weak Classifiers; Haar-like Features; Adaboost; Discriminant Function; Threshold;
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