A system to support the amateurs to takea delicious-looking picture of foods
Takao Kakimori
kakimori@onailab.com

Makoto Okabe
m.o@acm.org

Keiji Yanai
yanai@cs.uec.ac.jp

Rikio Onairikioonai@gmail.com
1. INTRODUCTION
Recently, many people take a picture of foods at home or inrestaurants, and upload the picture to a social networking
service (SNS) to share it with friends. People want to takea delicious-looking picture of foods, but it is often diﬃcult,because most of them have no idea how to take a deliciouslooking picture. There are many photography techniques
for composition[1], lighting, color, focus, etc, and the techniques used to take a picture are diﬀerent for diﬀerent typesof subjects. The problem lies in the diﬃculty for amateur
photographers to choose and apply appropriate ones fromsuch many techniques.
In this paper, we pay attention to composition and develop
a system to support the amateurs to take a delicious-looking
picture of foods in a short time. Our target users are theamateurs of food photography and our target photographic
subjects are foods on dishes. There are four steps to takea picture using our system: 1) our system automaticallyrecognizes foods on dishes; 2) our system suggests the composition and the camera tilt, by which the user can takea delicious-looking picture; 3) the user arranges foods anddishes on the table, and set the camera position and tilt; 4)
finally, the user takes the picture.

2. METHOD
There are a number of photography composition guidelines
to enhance the impact of a scene, e.g., ”place your subject
in the center”, ”use the diagonals”, ”use the Fibonacci spirals”, ”balancing with the rule of thirds”, etc. We assume
that these guidelines are useful for food photography. We
also assume that the composition suitable for a scene is determined by the following three parameters: 1) the shape ofthe dish (one of circle, ellipse, rect, and square), 2) ratio of
the area of foods to the area of the dish, and 3) the numberof side dishes.
Given the dishes, the user first takes the picture of all ofthem together. Our system then recognizes the name ofeach food by automatically analyzing the picture with deepconvolutional neural networks (DCNN) and our system es-Figure 1: The pictures of foods taken by our system.
Each pair shows the picture taken by our system(left) and suggested composition (right).
timates the three parameters. When the system fails to recognize them, the user can specifies the parameters manually.
The system then suggests the composition (the right panelin each pair of Figure 1). The blue area corresponds to themain dish, the green areas correspond to side dishes, andthe red line shows the composition rule. The user arranges
the dishes on the table according to the composition. Whentaking the picture, there is the symbol ”傾” at the bottom
right in the screen, which means inclination in Japanese.
When the color of the symbol is black, it means that thecamera tilt is not the suggested one. When the camera isin the suggested range of tilt, the symbol becomes purple.
This symbol helps the user to know the suggested camera
tilt interactively.

3.

RESULTS AND FUTURE WORK

We present an interface to take a delicious-looking picture offoods with food recognition. This system can let an amateur
decide appropriate composition. In top left of Figure 1, there
are foods pork cutlet, rice, miso soup and potato salada
in a picture. The proposed system uses Fibonacci spiraland diagonals. At the top right, The system uses ”rule ofthirds”. At the bottom, The system uses just diagonals. We
will evaluate the proposed system in the near future. We
plan two types of user studies. Both of them are subjective
evaluations: one is a usability study, and the other is toevaluate the quality of pictures taken by the test users withand without using our system.

4.

REFERENCES[1] Liu, L., Chen, R. and Cohen-Or, L. W. D.: Optimizing
Photo Composition, Computer Graphics Forum,Vol. 29, No. 2, pp. 469–478 (2010).