Rapid food recognition method based on Markov random field

Abstract

The invention discloses a rapid food recognition method based on a Markov random field, and mainly solves the problem that food is difficult to recognize due to the conditions that the food lacks of feature points and the shapes of the food are not fixed in food image recognition. The realization process of the rapid food recognition method comprises the following steps: (1) establishing a retrieval database, extracting feature descriptors in retrieval images, and constructing index files; (2) extracting feature descriptors of queried images; (3) calculating out the likelihood score of the queried images in food label types of the retrieval base according to the feature descriptors; (4) according to the likelihood score of the queried images in the label type food in the retrieval database and according to the condition probability between labels in menus of queried people, constructing a Markov energy formula and a smallest energy formula so as to obtain the food label type of the queried images. According to the rapid food recognition method disclosed by the invention, rapid and accurate recognition and classification can be performed on the food images, the food images can be easily expanded to a larger retrieval database and a queried image set, and the rapid food recognition method can be used for cultivating a healthy diet manner.

Description

technical field [0001] The invention belongs to the field of image information processing, in particular to a food recognition method, which can be used to solve the problems of food image analysis and food category recognition. Background technique [0002] With people's attention to dietary health, it is very necessary for those who want to improve their eating habits to record dietary information in a simple and easy way. So far, 24-hour food records have used the common method of recording "diet history" to assess an individual's eating habits. However, self-reporting methods are often inaccurate, especially among overweight people, some of whom routinely underreport their caloric intake. If the nutritional information of a diet can be automatically detected through food images, it will liberate users from manually recording information. [0003] Recently, in the field of "life-log" research, people are more and more interested in acquiring and processing information a...

AI Technical Summary

Problems solved by technology

However, since food images lack the above-mentioned meaningful feature points, and secondly, since the shape of real food is often amorphous, it is difficult to use the above method to judge the similarity of food shape and cannot accurately classify food

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