Multi-class Graph Boosting with Subgraph Sharing for Object Recognition
Multi-class Graph Boosting with Subgraph Sharing for Object Recognition
复制标题
用于对象识别的具有子图共享的多类图增强
DOI：
10.1109/icpr.2010.381
复制
发表时间：
2010
期刊：
影响因子：
--
通讯作者：
Yun Yang
中科院分区：
文献类型：
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作者：
Bang Zhang;G. Ye;Yang Wang;Wei Wang;Jie Xu;G. Herman;Yun Yang
本文提出了一种新的多类图增强算法来识别不同的视觉对象。该方法以子图为特征构造基分类器，并利用流行的纠错输出码方案解决多类问题。同时考虑了基分类器和纠错编码矩阵两个因素。子图可以被不同的类共享，因此被明智地用于提高分类性能。对多类目标识别的实验结果表明了该算法的有效性。
In this paper, we propose a novel multi-class graph boosting algorithm to recognize different visual objects. The proposed method treats subgraph as feature to construct base classifier, and utilizes popular error correcting output code scheme to solve multi-class problem. Both factors, base classifier and error-correcting coding matrix are considered simultaneously. And subgragphs, which are shareable by different classes, are wisely used to improve the classification performance. The experimental results on multi-class object recognition show the effectiveness of the proposed algorithm.
DOI：
10.1613/jair.105
发表时间：
1994-08
期刊：
ArXiv
影响因子：
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作者：
Thomas G. Dietterich;Ghulum Bakiri
通讯作者：
Thomas G. Dietterich;Ghulum Bakiri