Lili Pan1, Cong Li1, *, Samira Pouyanfar2, Rongyu Chen1, Yan Zhou1
CMC-Computers, Materials & Continua, Vol.62, No.2, pp. 731-746, 2020, DOI:10.32604/cmc.2020.06508
Abstract With the development of deep learning and Convolutional Neural Networks
(CNNs), the accuracy of automatic food recognition based on visual data have
significantly improved. Some research studies have shown that the deeper the model is,
the higher the accuracy is. However, very deep neural networks would be affected by the
overfitting problem and also consume huge computing resources. In this paper, a new
classification scheme is proposed for automatic food-ingredient recognition based on
deep learning. We construct an up-to-date combinational convolutional neural network
(CBNet) with a subnet merging technique. Firstly, two different neural networks are
utilized for learning interested features.… More >
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