Food recognition method at intelligent terminal

Abstract

The invention discloses a food recognition method at an intelligent terminal, and the method is based on a convolution neural network method, and comprises a training process and an automatic classification process. The training process comprises the following steps: firstly building a training sample set; secondly constructing a network structure model according to a classic AlexNet network; thirdly carrying out network training through an open-source Caffe network framework, and continuously adjusting initial conditions so as to obtain an optimal network structure model and parameter configuration. The automatic classification process comprises the steps: enabling a to-be-recognized image photographed by a user to serve as an input image of the network; configuring the network according to parameters of a trained network structure in a computer, and classifying input images; and finally displaying the former 10 optimal classification results to the user. The method can achieve the automatic recognition of the type of food at the intelligent terminal, is high in speed, is small in storage capacity, is high in accuracy, and is good in user experience.

Description

technical field [0001] The invention relates to the field of artificial intelligence, in particular to a food recognition method on an intelligent terminal. Background technique [0002] Image classification is an important field of computer vision. At present, great progress and development have been made in this field. Automatic image classification can facilitate our life and improve our quality of life. There have been many invention patents in automatic image classification, but many of these patents use traditional feature extraction methods, that is, by extracting features such as SIFT features, DenseSIFT features, HOG features, etc., and then constructing dictionaries through k-means clustering methods Encode the features, and then train through the support vector machine (SVM) method to obtain multiple SVMs, and finally use these SVMs to classify the test pictures to obtain results, such as the image classification method extracted from the patent CN104077597A, usin...

AI Technical Summary

Problems solved by technology

In addition, more and more people are beginning to pay attention to their own health issues. People hope to have a system software that can quickly and accurately record food. Traditi

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