Effects of Increasing Background to Foreground Ratio in Food Image Recognition

Abstract

Food image recognition is very useful in nowadays applications. It has been shown recently that the recognition accuracy depends on the ratio between background and foreground of the food image. In this work, the food image recognition using ResNet with FOOD-101 dataset is studied. The food area in the image is determined using ENet. The effect of the increase of the ratio between background and foreground of food images in terms of recognition accuracy is determined. It is found that increasing such ratio from the inner food area of the image can help increasing the accuracy. An average of 2.5% for increasing the accuracy is obtained when increasing the ratio of 100% from the inner ratio. Additionally, a higher accuracy can also be achieved compared to the overall full image recognition accuracy, for the case of inner food area ratio with 50% increasing ratio. These results show that with a different food image input, it is possible to increase the food image recognition accuracy.