| 基于特征加权和支持向量机的图像分类研究 |
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| 引用本文： | 杜娟,孙君顶.基于特征加权和支持向量机的图像分类研究[J].激光与红外,2013,43(3):315-308. |
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| 作者姓名： | 杜娟 孙君顶 |
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| 作者单位： | 1. 河南理工大学计算机科学与技术学院,河南焦作,454000
2. 河南理工大学计算机科学与技术学院,河南焦作454000;“图像处理与图像通信”江苏省重点实验室,江苏南京210003 |
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| 基金项目： | 教育部科学技术研究重点项目（No.210128）;河南省骨干教师资助计划（No.2010GGJS-059）; “图像处理与图像通信”江苏省重点实验室基金（No.LBEK2011002）资助 |
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| 摘 要： | 利用图像特征加权方法和支持向量机实现了图像的有效分类。首先根据特征的稳定性来判断特征的重要程度,从而赋予不同权重;然后借助支持向量机实现图像分类;最后采用不同颜色和纹理特征验证了在特征加权和不加权情况下图像分类的准确程度。实验结果表明本文的方法有效提高了图像分类的准确性。
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| 关 键 词： | 底层特征 支持向量机 特征加权 图像分类 |
Image classification based on feature weighting and support vector machine |
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| DU Juan,SUN Jun-ding.Image classification based on feature weighting and support vector machine[J].Laser & Infrared,2013,43(3):315-308. |
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| Authors: | DU Juan SUN Jun-ding |
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| Affiliation: | School of Computer Science and technology,Henan Polytechnic University,Jiaozuo 454003,China;Image Processing & Image Communication Lab of Jiangsu Province,Nanjing 210003,China |
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| Abstract: | A new effective method for image classification is presented in the paper based on feature weighting and support vector machine.First of all,each dimension of the feature is set different weight according to its stability judged by the degree of its importance.And then,the images are classified based on the weighted feature and support vector machine.Finally,different color and texture features are adopted to verify the classification accuracy in the case of feature weighted and unweighted.Experimental results show that the proposed method is effective to improve the accuracy of image classification. |
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| Keywords: | low-level features support vector machine feature weighting image classification |
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