A Theoretical Approach to Construct Highly Discriminative Features with Application in AdaBoost

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A Theoretical Approach to Construct Highly Discriminative Features with Application in AdaBoost

Jin, Yuxin / Tao, Linmi / Xu, Guangyou / Peng, Yuxin

AdaBoost is a practical method of real-time face detection, but abides by a crucial problem of overfitting for the big number of features used in a trained classifier due to the weak discriminative abilities of these features. This paper proposes a theoretical approach to construct highly discriminative features, which is named composed features, from Haar-like features. Both of the composed and Haar-like features are employed to train a multi-view face detector. The primary experiments show promising results in reducing the number of features used in a classifier, which leads to the increase of the generalization ability of the classifier.

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Document information

Title :

A Theoretical Approach to Construct Highly Discriminative Features with Application in AdaBoost

Contributors:

Yagi, Yasushi (editor) / Kang, Sing Bing (editor) / Kweon, In So (editor) / Zha, Hongbin (editor) / Jin, Yuxin (author) / Tao, Linmi (author) / Xu, Guangyou (author) / Peng, Yuxin (author)

Conference:

Asian Conference on Computer Vision ; 2007 ; Tokyo, Japan November 18, 2007 - November 22, 2007

Published in:

Computer Vision – ACCV 2007 ; Chapter : 71 ; 748-757

Lecture Notes in Computer Science ; 4843

Publication date :

2007-01-01

Size :

10 pages

ISBN :

978-3-540-76386-4 , 978-3-540-76385-7

ISSN :