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http://dx.doi.org/10.7840/kics.2013.38A.6.486

A Fast and Efficient Haar-Like Feature Selection Algorithm for Object Detection  

Chung, Byung Woo (서강대학교 전자공학과 CAD&ES 연구실)
Park, Ki-Yeong (서강대학교 전자공학과 CAD&ES 연구실)
Hwang, Sun-Young (서강대학교 전자공학과 CAD&ES 연구실)
Publication Information
The Journal of Korean Institute of Communications and Information Sciences / v.38A, no.6, 2013 , pp. 486-491 More about this Journal
Abstract
This paper proposes a fast and efficient Haar-like feature selection algorithm for training classifier used in object detection. Many features selected by Haar-like feature selection algorithm and existing AdaBoost algorithm are either similar in shape or overlapping due to considering only feature's error rate. The proposed algorithm calculates similarity of features by their shape and distance between features. Fast and efficient feature selection is made possible by removing selected features and features with high similarity from feature set. FERET face database is used to compare performance of classifiers trained by previous algorithm and proposed algorithm. Experimental results show improved performance comparing classifier trained by proposed method to classifier trained by previous method. When classifier is trained to show same performance, proposed method shows 20% reduction of features used in classification.
Keywords
Haar-like Feature; Machine Learning; Classifier; Feature Selection; Object Detection;
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Times Cited By KSCI : 1  (Citation Analysis)
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