This paper proposes a method for estimating the attractiveness of food photos in order to assist a user to shoot them attractively. The proposed method extracts both color and shape features from input food images, and then integrates them according to a regression scheme. By this way, the proposed method estimates the attractiveness of an unknown food photo. We also created a food image dataset taken from various 3D-angles for each food category, and set target values of their attractiveness through subjective experiments. Then, we evaluated the performance of the proposed method in two different ways of constructing the attractiveness estimator: One that constructs it for each food category, and the other that constructs a common attractiveness estimator for all food categories. Experimental results showed the effectiveness of the proposed method in addition to the necessity for adaptively selecting the estimator depending on the appearance of foods for further performance improvement.