基于稀疏表示和决策融合的图像分类方法

利用多个稀疏表示分类器融合的决策信息对图像进行分类,可避免单个特征对图像分类的影响。提出一种自适应调节权重的多稀疏分类器融合图像分类方法。对原始图像分别提取3组不同特征,并训练出各自稀疏表示分类器;根据各个子分类器的准确率,通过迭代计算自适应确定各分类器最终权重;融合各子分类器的输出结果进行最终类别判断。基于Cifar-10图像数据集进行多组实验,结果表明,相对仅提取单特征的图像分类方法,该方法有效提高了图像分类准确率。

IMAGE CLASSIFICATION METHOD BASED ON SPARSE REPRESENTATION AND DECISION FUSION

Using multiple sparse representation classifiers to combine decision information to classify images can avoid the impact of individual features on image classification. Thus,this paper proposed a multi-sparse classifier fusion image classification method with adaptive adjustment weights. Three sets of different features were extracted from the original image,and the respective sparse representation classifiers were trained. According to the accuracy of each sub-classifier,the final weight of each classifier was determined adaptively by iterative calculation. Finally,the output of each sub-classifier was merged for final category judgment. Multiple sets of experiments were performed based on the Cifar-10 image data set. The results show that the proposed can effectively improve the image classification accuracy,compared with the image classification method only extracting single feature.