BOOSTING KERNEL COMBINATION FOR MULTI-CLASS IMAGE CATEGORIZATION

被引：0

作者：

Lechervy, Alexis ^{[1
]}

Gosselin, Philippe-Henri ^{[1
]}

Precioso, Frederic ^{[2
]}

机构：

[1] Univ Cergy Pontoise, ETIS, CNRS, ENSEA, 6 Ave Ponceau,BP44, F-95014 Cergy Pontoise, France

[2] UNS CNRS, UMR7271, I3S, F-06903 Sophia Antipolis, France

来源：

2012 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP 2012) | 2012年

关键词：

Image databases; Machine learning algorithms; Boosting;

D O I：

暂无

中图分类号：

TB8 [摄影技术];

学科分类号：

0804 ;

摘要：

In this paper, we propose a novel algorithm to design multi-class kernel functions based on an iterative combination of weak kernels in a scheme inspired from boosting framework. The method proposed in this article aims at building a new feature where the centroid for each class are optimally located. We evaluate our method for image categorization by considering a state-of-the-art image database and by comparing our results with reference methods. We show that on the Oxford Flower databases our approach achieves better results than previous state-of-the-art methods.

引用

页码：1893 / 1896

页数：4

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