Name
Abstract | ||
|---|---|---|
Diet management is a key factor for the prevention and treatment of diet-related chronic diseases. Computer vision systems aim to provide automated food intake assessment using meal images. We propose a method for the recognition of already segmented food items in meal images. The method uses a 6-layer deep convolutional neural network to classify food image patches. For each food item, overlapping patches are extracted and classified and the class with the majority of votes is assigned to it. Experiments on a manually annotated dataset with 573 food items justified the choice of the involved components and proved the effectiveness of the proposed system yielding an overall accuracy of 84.9%. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-23222-5_56 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
Food recognition,Convolutional neural networks,Dietary management,Machine learning | Pattern recognition,Convolutional neural network,Food recognition,Computer science,Artificial intelligence,Dietary management,Dietary assessment,Machine learning | Conference |
Volume | ISSN | Citations |
9281 | 0302-9743 | 8 |
PageRank | References | Authors |
0.58 | 17 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| S Christodoulidis | 1 | 160 | 10.20 |
| Marios Anthimopoulos | 2 | 247 | 13.75 |
| Stavroula G Mougiakakou | 3 | 342 | 28.61 |