Rongyu Chen1, Lili Pan1, *, Cong Li1, Yan Zhou1, Aibin Chen1, Eric Beckman2
CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1691-1706, 2020, DOI:10.32604/cmc.2020.011706
Abstract With the development of Deep Convolutional Neural Networks (DCNNs), the
extracted features for image recognition tasks have shifted from low-level features to the
high-level semantic features of DCNNs. Previous studies have shown that the deeper the
network is, the more abstract the features are. However, the recognition ability of deep
features would be limited by insufficient training samples. To address this problem, this
paper derives an improved Deep Fusion Convolutional Neural Network (DF-Net) which
can make full use of the differences and complementarities during network learning and
enhance feature expression under the condition of limited datasets. Specifically, DF-Net
organizes two… More >
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