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。
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 resuhs show that the proposed method is effective to improve the ac- curacy of image classification.
出处
《激光与红外》
CAS
CSCD
北大核心
2013年第3期315-318,共4页
Laser & Infrared
基金
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
关键词
底层特征
支持向量机
特征加权
图像分类
low-level features
support vector machine
feature weighting
image classification
