KEYWORDS: Visualization, Scene classification, Information fusion, Feature extraction, Information visualization, Convolution, Convolutional neural networks, Human vision and color perception, Information technology, Neural networks
A multi-scale binocular-channels convolution neural network (MBCNN) is proposed to solve complex scene classification and achieved a good accuracy. We use a physiological phenomenon called visual crowding to explain the deficiency of the CNN framework and prove the effectiveness of the double flow model. With the help of a novel bilateral-channels network based on global information and local significant information and our multi-scale feature integration method, the proposed MBCNN can reduce the identification obstacle caused by visual crowding in the V1(Information input area) and V4 (High-level information area) area separately. Experiment results verify that the proposed network has better performance on MIT Indoor 67 and Scene 15 classification datasets.
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