Richer feature for image classification with super and sub kernels based on deep convolutional neural network

摘要

Deep convolutional neural network (DCNN) has obtained great successes for image classification. However, the principle of human visual system (HVS) is not fully investigated and incorporated into the current popular DCNN models. In this work, a novel DCNN model named parallel crossing DCNN (PC-DCNN) is designed to simulate HVS with the concepts of super convolutional kernel and sub convolutional kernel being introduced. Moreover, a multi-scale PC-DCNN (MS-PC-DCNN) framework is designed, with which a batch of PC-DCNN models are deployed and the scores from each PC-DCNN model are fused by weighted average for the final prediction. The experimental results on four public datasets verify the superiority of the proposed model as compared to a number of state-of-the-art models. (C) 2017 Elsevier Ltd. All rights reserved.