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Proceedings Paper

Boosted distance based on local and global dissimilarity representation
Author(s): H. Yin; Y. F. Cao; H. Sun
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Paper Abstract

We propose a new distance estimation technique by boosting and apply it to enhance the effectiveness of classifier when the training set is insufficient. The proposed method is called Boosted Distance based on local and global dissimilarity representation (BDLGDR). It is a modified method of Boosted Distance. Rather than simply differentiating the feature vectors, we calculate a new dissimilarity representation of each couple of feature vectors. This new dissimilarity representation contains two parts: local dissimilarity representation part and global dissimilarity representation part. The proposed method does not only achieve high classification accuracy when the training set is insufficient but when the number of training set is sufficient it also can achieve as high accuracy as AdaBoost. The method has been thoroughly tested on several databases of high-resolution (1.25m) Terra-SAR images. In the first experiment, we decreased the number of the training sample per class from 10 to 1. The result showed that the proposed method outperformed both Boosted Distance and AdaBoost. In the second experiment, we used sufficient training samples. The experimental result illuminated that the proposed method performed at least as well as AdaBoost and needed fewer iteration rounds to converge than Boosted Distance.

Paper Details

Date Published: 30 October 2009
PDF: 8 pages
Proc. SPIE 7496, MIPPR 2009: Pattern Recognition and Computer Vision, 74960X (30 October 2009); doi: 10.1117/12.833006
Show Author Affiliations
H. Yin, Wuhan Univ. (China)
Y. F. Cao, Wuhan Univ. (China)
H. Sun, Wuhan Univ. (China)


Published in SPIE Proceedings Vol. 7496:
MIPPR 2009: Pattern Recognition and Computer Vision

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