Food Image Recognition Based on Convolutional Neural Network

Aiming to solve the problems of the traditional food image recognition methods, such as their poor extraction ability, poor accuracy and poor running efficiency, as well as the difficulty with the convolutional neural network in identifying similar food images, a new food image recognition model ChinaFood-CNN is proposed to realize the accurate classification of food. Drawing on the research on the multi-classification loss function SoftmaxWithLoss and taking into account the intra-class similarity of food images, the maximum class-space loss function (MCSWithLoss) is proposed to increase the distance between similar classes and achieve the similar class distinction. To solve the redundancy problem of training set in random sample selection, negative sample selection is used in network model training. Experimental results show that the accuracy of the model for food images is 69.2%, which is 17.6%, 16.8% and 3.6% higher than that of AlexNet, VGG16 and ResNet respectively.