Food images have been receiving increased attention in recent dietary control methods. We present the current status of our web-based system that can be used as a dietary management support system by ordinary Internet users. The system analyzes image archives of the user to identify images of meals. Further image analysis determines the nutritional composition of these meals and stores the data to form a Foodlog. The user can view the data in different formats, and also edit the data to correct any mistakes that occurred during image analysis. This paper presents detailed analysis of the performance of the current system and proposes an improvement of analysis by pre-classification and personalization. As a result, the accuracy of food balance estimation is significantly improved.

In this paper, a food-logging system that can distinguish food images from other images, analyze the food balance, and visualize the log is presented. The image processing is based on feature vectors consisting of color histograms, DCT coefficients, detected image patterns and so forth. Support Vector Machine (SVM) was used to detect food images and to analyze the food balance. Experimental results show that the food image extraction presents above 88% of accuracy and the food balance estimation is achieved with more than 73% of accuracy.