Image Classification with Local Linear Decoding and Global Multi-feature Fusion

Recent years have witnessed a surge of interest in image classification. The combination of deep neural network with feature extraction has improved image classification performance dramatically. In order to improve the performance of image classification, this paper proposes an image classification algorithm based on deep neural network of linear decoder and softmax regression model. First, we learn features of some small image patches with linear decoder; secondly, by convolving and pooling the large images with the learned features, then we obtain the pooled convolved features; thirdly, we use softmax regression model to learn the features for image classification. Experimental results are encouraging and demonstrate the validity and superiority of our method.