基于TWSVM的图像分类

Image Classification Based on Twin Support Vector Machines

图像分类技术是图像数据处理中最重要的技术之一.支持向量机是基于统计学习理论而提出的机器学习算法,在样本数少的时候能达到很好的分类效果.孪生支持向量机是基于支持向量机而提出来的,其性能优于支持向量机.通过提取彩色图像的颜色特征与纹理特征,利用孪生支持向量机与支持向量机对这些特征向量进行分类,孪生支持向量机的分类准确率与稳定性都高于支持向量机.

The image classification is one of the most important technologies of image data processing. Support Vector Machine is a machine learning algorithm based on statistical learning theory,which can achieve a good classification results in the small sample size. Twin Support Vector Machines is based on Support Vector Machine,which is superior to Support Vector Machine. By extracting color features and texture features of images,using Twin Support Vector Machines and Support Vector Machine to classify these feature vectors,the results shows that the accuracy and stability of Twin Support Vector Machines is higher than Support Vector Machine.