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We have investigated the “FoodLog” multimedia food-recording tool, whereby users upload photographs of their meals and a food diary is constructed using image-processing functions such as food-image detection and food-balance estimation. In this paper, following a brief introduction to FoodLog, we propose a Bayesian framework that makes use of personal dietary tendencies to improve both food-image detection and food-balance estimation. The Bayesian framework facilitates incremental learning. It incorporates three personal dietary tendencies that influence food analysis: likelihood, prior distribution, and mealtime category. In the evaluation of the proposed method using images uploaded to FoodLog, both food-image detection and food-balance estimation are improved. In particular, in the food-balance estimation, the mean absolute error is significantly reduced from 0.69 servings to 0.28 servings on average for two persons using more than 200 personal images, and 0.59 servings to 0.48 servings on average for four persons using 100 personal images. Among the works analyzing food images, this is the first to make use of statistical personal bias to improve the performance of the analysis.