The intelligent analysis of a user's diet via computer is a meaningful research topic.Traditional methods have focused on food recognition.However

China is a country with diverse food styles.Therefore

designing a generic food recognition method is difficult.This study focuses on food material recognition instead of food recognition.Thus

a FOOD-SCUT image data set is initially collected.This data set includes 70 types of food material that are commonly seen in people's daily lives.The total number of images is 8015. This study extracts different traditional features and uses different classification methods in implementing food material recognition to compare the experimental performance.The selected features include scale-invariant feature transform

color histogram features

histograms of oriented gradients

speeded-up robust features (SURF)

local binary patterns

and Gabor features.All features are further encoded using the bag-of-words method

and the selected classifiers include the support vector machine

native Bayesian classifier

random forest

and K-nearest neighbor classifier.Moreover

this study designs a deep neural network for food material recognition. Various experiments have been performed to test the performance of the proposed food material recognition using different combinations of traditional features.By adjusting different features combination and selecting an appropriate classifier and its optimum parameters

the best recognition accuracy can be achieved using the proposed method based on the traditional features.Experiments further test the performance of the proposed method using a deep convolution neural network.The deep convolution neural network includes two different training modes

namely

direct training and pretraining modes.The best performance can be achieved after selecting different numbers of layer and adjusting the weight initialization method.The final experimental results show that the proposed method can achieve 88.98% recognition accuracy based on the traditional features.However

using the deep convolution neural network can achieve a recognition accuracy of 95.7%

which is 6.72% higher than the proposed method based on the traditional features. The statistical analysis of the FOOD-SCUT data set in this study shows the diversity within classes and the similarity between classes.Therefore

this FOOD-SCUT data set can be used for further food material analysis.Moreover

the high application of the FOOD-SCUT data set can provide all original images for the further implementation of food material application.Moreover

the experimental results can provide suggestions concerning the selection of models and parameters to reduce the developing time in future studies.