A. L. C. Barczak
ABSTRACT
We propose a mixed structure to form cascades for AdaBoost classifiers, where parallel strong classifiers are trained for each layer. The structure allows for rapid training and guarantees high hit rates without changing the original threshold. We implemented and tested the approach for two datasets from UCI [1], and compared results of binary classifiers using three different structures: standard AdaBoost, a cascade classifier with threshold adjustments, and the proposed structure.
- A. Asuncion and D. Newman. UCI machine learning repository. University of California, Irvine, School of Information and Computer Sciences, 2007. http://www.ics.uci.edu/~mlearn/MLRepository.html.Google Scholar
- Y. Freund and R. E. Schapire. A short introduction to boosting. Journal of Jap. Society for Art Intell., 14(5):771--780, 1999.Google Scholar
- R. Lienhart, L. Liang, and A. Kuranov. A detector tree of boosted classifiers for real-time object detection and tracking. In ICME2003, pages 277--280. IEEE, 2003. Google ScholarDigital Library
- P. Viola and M. Jones. Robust real-time face detection. International Journal of Computer Vision, 57:137--154, 2004. Google ScholarDigital Library
Index Terms
- Empirical evaluation of a new structure for AdaBoost
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