Image Classification Using Convolutional Neural Networks With Multi-stage Feature

Convolutional neural networks (CNN) have been widely used in automatic image classification systems. In most cases, features from the top layer of the CNN are utilized for classification; however, those features may not contain enough useful information to predict an image correctly. In some cases, features from the lower layer carry more discriminative power than those from the top. Therefore, applying features from a specific layer only to classification seems to be a process that does not utilize learned CNN's potential discriminant power to its full extent. This inherent property leads to the need for fusion of features from multiple layers. To address this problem, we propose a method of combining features from multiple layers in given CNN models. Moreover, already learned CNN models with training images are reused to extract features from multiple layers. The proposed fusion method is evaluated according to image classification benchmark data sets, CIFAR-10, NORB, and SVHN. In all cases, we show that the proposed method improves the reported performances of the existing models by 0.38%, 3.22% and 0.13%, respectively.