Diet Image Analysis for Automated Food Recognition
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional diet management methods are tedious and reduce user willingness to engage in diet tracking, leading to ineffective health management, especially in preventing gastrointestinal and cardiovascular diseases, as they fail to provide a user-friendly and efficient way to analyze diet habits and characteristics.
Innovation Solution
A computer-implemented method and system that captures diet images, preprocesses them to identify food segments, extracts features, determines diet types, and provides personal diet characteristic analysis, using image analysis techniques like LBP, SIFT, and HoG algorithms to facilitate user-friendly diet management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional diet management methods use manual record-filling of personal data sheets, then diet information can be collected, but the process becomes too tedious and reduces user willingness to engage
Solution Approach 1:
The patent replaces the mechanical manual record-filling process with an automated image-based recognition system. Users simply capture photos of their meals, and the system automatically identifies food types, portions, and nutritional information, eliminating the tedious manual data entry while maintaining comprehensive diet information collection
Solution Approach 2:
The system enables self-service diet management by allowing users to autonomously capture and analyze their own diet images without requiring manual intervention or expert analysis. The automated processing provides personalized diet feedback that users can obtain independently, increasing engagement while maintaining information quality
2Measurement precision
If detailed manual diet recording is implemented, then comprehensive diet analysis can be achieved, but the complexity and time required increase significantly
Solution Approach 1:
The system performs preliminary action by pre-processing and analyzing diet images in real-time as users capture them. The automated recognition and nutritional analysis occur immediately during the meal recording process, providing comprehensive diet analysis without requiring subsequent manual processing time
Solution Approach 2:
The patent substitutes time-consuming manual diet recording and analysis with automated image processing and machine learning algorithms. The system rapidly processes captured images to extract detailed nutritional information, maintaining high measurement precision while dramatically reducing the time users need to invest
3Productivity
If automated image-based diet analysis is implemented, then user engagement and efficiency improve, but the system complexity increases
Solution Approach 1:
The patent introduces an intermediary layer of automated image processing and machine learning models that bridge the simple user action of photo capture with the complex task of nutritional analysis. This intermediary system handles the computational complexity internally while presenting a simple interface to users, maintaining high productivity without requiring users to understand or manage the underlying system complexity
Data Source
AI summary
An electronic device, a system and a method for diet management based on image analysis are provided. The system includes a computer and a database. The computer comprises a processor for performing the following operations: capturing at least one diet image via an image capture device; pre-processing the at least one diet image so as to obtain at least one diet region from the at least one diet image and obtain at least one detailed food segment from the diet region; extracting at least one diet image feature from the at least one detailed food segment; determining a diet type of the at least one detailed food segment based on the at least one diet image feature; and providing a personal diet characteristic analysis based on the diet type and an area of the at least one detailed food segment.