Learning Algorithm for Multiple Distribution Data using Haar-like Feature and Decision Tree


KIPS Transactions on Software and Data Engineering, Vol. 2, No. 1, pp. 43-48, Jan. 2013
10.3745/KTSDE.2013.2.1.43,   PDF Download:

Abstract

Adaboost is widely used for Haar-like feature boosting algorithm in Face Detection. It shows very effective performance on single distribution model. But when detection front and side face images at same time, Adaboost shows it`s limitation on multiple distribution data because it uses linear combination of basic classifier. This paper suggest the HDCT, modified decision tree algorithm for Haar-like features. We still tested the performance of HDCT compared with Adaboost on multiple distributed image recognition.


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Cite this article
[IEEE Style]
C. H. Lee, J. H. Kawak, L. Y. Woen, "Learning Algorithm for Multiple Distribution Data using Haar-like Feature and Decision Tree," KIPS Transactions on Software and Data Engineering, vol. 2, no. 1, pp. 43-48, 2013. DOI: 10.3745/KTSDE.2013.2.1.43.

[ACM Style]
Chang Hoon Lee, Ju Hyun Kawak, and Li Young Woen. 2013. Learning Algorithm for Multiple Distribution Data using Haar-like Feature and Decision Tree. KIPS Transactions on Software and Data Engineering, 2, 1, (2013), 43-48. DOI: 10.3745/KTSDE.2013.2.1.43.