关键词: 图像分类, 标签传播, 典型相关分析, 多视图, 半监督

Abstract: In order to improve the performance of classification by using information of multi-view features in semi-supervised scenario, firstly, by minimizing local reconstruction error of input feature vectors, proper edge weights can be learnt for the graph which is generated by using input feature vectors as vertexes of graph. Then, the edge weights are used for semi-supervised learning. This paper applies the manifold structure of input data which is captured by minimizing local reconstruction error of input feature vectors for semi-supervised learning, which is beneficial to improve the accuracy of label prediction in semi-supervised learning. For the using of multi-view features of training images, firstly, with the help of the technique of improved canonical correlation analysis, multi-view features with more discriminant information can be learnt, then the multi-view features with more discriminant information are used for image classification tasks by effectively fusing. Experimental results demonstrate that the proposed method can effectively perform discriminant tasks by well exploring discriminant information of multi-view feature representations of training samples in semi-supervised scenario.

Key words: image classification, label propagation, canonical correlation analysis, multi-view, semi-supervised