Automatic product image classification with multiple support vector machine classifiers

被引：2
|
作者：
Jia S.-J. [1 ,2 ]
Kong X.-W. [1 ]
Man H. [1 ]
机构：
[1] Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116023, Liaoning
[2] College of Electrical and Information, Dalian Jiaotong University, Dalian 116028, Liaoning
来源：
Journal of Shanghai Jiaotong University (Science) | 2011年 / 16卷 / 4期
关键词：
Multiple SVM classifier; Product image classification; Support vector machine (SVM);
D O I：
10.1007/s12204-011-1180-x
中图分类号：
学科分类号：
摘要：
For the task of visual-based automatic product image classification for e-commerce, this paper constructs a set of support vector machine (SVM) classifiers with different model representations. Each base SVM classifier is trained with either different types of features or different spatial levels. The probability outputs of these SVM classifiers are concatenated into feature vectors for training another SVM classifier with a Gaussian radial basis function (RBF) kernel. This scheme achieves state-of-the-art average accuracy of 86.9% for product image classification on the public product dataset PI 100. © Shanghai Jiaotong University and Springer-Verlag Berlin Heidelberg 2011.
引用
收藏
页码：391 / 394
页数：3
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