KSC 2020
Current Result Document : 12 / 12
| 한글제목(Korean Title) |
An Enhanced Densely Connected Convolutional Network for Food Recognition |
| 영문제목(English Title) |
An Enhanced Densely Connected Convolutional Network for Food Recognition |
| 저자(Author) |
UWIZEYE Delphine
Oh-Heum Kwon
Ha-Joo Song
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| 원문수록처(Citation) |
VOL 47 NO. 02 PP. 0093 ~ 0095 (2020. 12) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Food recognition is essential in nutritional assessment for eating more healthily, avoiding some diseases, and managing weight loss. However, due to the nature of the food, it is a challenging task to recognize some types of food. Deep learning has been widely used for food recognition, as it outperformed other approaches. In this paper, we propose an enhanced densely connected convolutional network (DenseNet) approach for food image recognition. For performance tests, we compared our model with Resnet152 using UEC-FOOD256 dataset. The test results show that our model outperforms other methods with an accuracy of 92.23% on the training set and 65.30% on the validation set. |
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