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Proceedings Paper

Fusion of local and global features for efficient object detection
Author(s): Duy-Dinh Le; Shin'ichi Satoh
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Paper Abstract

In this paper, we propose a two-stage method for efficient object detection that combines full advantages of AdaBoost and SVM to achieve a reasonable balance in both training and classification speed. In the first stage, we use Haar wavelet features and AdaBoost to train a cascade of classifiers for quick and efficient rejection. This cascade of classifiers consists of simple-to-complex classifiers that allow adapting to complexity of input patterns, rejects almost 90%-99% non-object patterns rapidly. Hard patterns, object-like patterns, which are passed through the first stage, will be classified by the second stage which uses a non linear SVM-based classifier with pixel-based global features. The nonlinear SVM classifier is robust enough in order to reach high performance. We have investigated our proposed method to detect different kinds of objects such as face and facial features like eye and mouth regions. In training, our system is roughly 25 times faster than the system trained by AdaBoost. In running, the experimental results show 1,000 times faster than SVM based method and slightly slower than AdaBoost based method with a comparable accuracy.

Paper Details

Date Published: 23 February 2005
PDF: 11 pages
Proc. SPIE 5673, Applications of Neural Networks and Machine Learning in Image Processing IX, (23 February 2005); doi: 10.1117/12.586592
Show Author Affiliations
Duy-Dinh Le, The Graduate Univ. for Advanced Studies (Japan)
Shin'ichi Satoh, The Graduate Univ. for Advanced Studies (Japan)
National Institute of Informatics (Japan)


Published in SPIE Proceedings Vol. 5673:
Applications of Neural Networks and Machine Learning in Image Processing IX
Nasser M. Nasrabadi; Syed A. Rizvi, Editor(s)

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