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


Vol. 38,  No. 6, pp. 486-491, Jun.  2013


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  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.

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  Cite this article

[IEEE Style]

B. W. Chung, K. Park, S. Hwang, "A Fast and Efficient Haar-Like Feature Selection Algorithm for Object Detection," The Journal of Korean Institute of Communications and Information Sciences, vol. 38, no. 6, pp. 486-491, 2013. DOI: .

[ACM Style]

Byung Woo Chung, Ki-Yeong Park, and Sun-Young Hwang. 2013. A Fast and Efficient Haar-Like Feature Selection Algorithm for Object Detection. The Journal of Korean Institute of Communications and Information Sciences, 38, 6, (2013), 486-491. DOI: .

[KICS Style]

Byung Woo Chung, Ki-Yeong Park, Sun-Young Hwang, "A Fast and Efficient Haar-Like Feature Selection Algorithm for Object Detection," The Journal of Korean Institute of Communications and Information Sciences, vol. 38, no. 6, pp. 486-491, 6. 2013.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880