Research Article
Evaluation of VGG Networks for Semantic Image Segmentation of Malaysian Meals
@INPROCEEDINGS{10.4108/eai.18-7-2019.2287943, author={N Jamil and N AN N Redzuan and M F Ismail and W AW Ramli}, title={Evaluation of VGG Networks for Semantic Image Segmentation of Malaysian Meals}, proceedings={Proceedings of the 1st International Conference on Informatics, Engineering, Science and Technology, INCITEST 2019, 18 July 2019, Bandung, Indonesia}, publisher={EAI}, proceedings_a={INCITEST}, year={2019}, month={10}, keywords={food images malaysian meals}, doi={10.4108/eai.18-7-2019.2287943} }- N Jamil
N AN N Redzuan
M F Ismail
W AW Ramli
Year: 2019
Evaluation of VGG Networks for Semantic Image Segmentation of Malaysian Meals
INCITEST
EAI
DOI: 10.4108/eai.18-7-2019.2287943
Abstract
This paper evaluates VGG-16 and VGG-19 networks in performing semantic image segmentation of Malaysian meals. This is a preliminary investigation of using transfer learning models to recognize food objects in typical Malaysian meals. Most current works of food recognition system calculate the calories and nutritional content of a meal based on the food object recognition, regardless of the portion size. Our final aim is to develop a food recognition system that considers the portion size in calculating the calories and nutritional content. Therefore, semantic segmentation of the food objects in the meal is a very important stage. Our work also initiated the training datasets for Malaysian meals that will be made available to the public. Using a small training dataset and a basic configuration of the VGG network, our results show inconsistent findings of the performance of VGG-16 and VGG-19. These findings will serve as a fundamental guideline to improve the semantic segmentation of food images.
