Name
Abstract | ||
|---|---|---|
Image-based food recognition pose new challenges for mainstream computer vision algorithms. Recent works in the field focused either on hand-crafted representations or on learning these by exploiting deep neural networks (DNN). Despite the success of DNN-based works, these exploit off-the-shelf deep architectures which are not cast to the specific food classification problem. We believe that better results can be obtained if the architecture is defined with respect to an analysis of the food composition. Following such an intuition, this work introduces a new deep scheme that is designed to handle the food structure. In particular, we focus on the vertical food traits that are common to a large number of categories (i.e., 15% of the whole data in current datasets). Towards the final objective, we first introduce a slice convolution block to capture such specific information. Then, we leverage on the recent success of deep residual blocks and combine those with the sliced convolution to produce the classification score. Extensive evaluations on three benchmark datasets demonstrated that our solution has better performance than existing approaches (e.g., a top-1 accuracy of 90.27% on the Food-101 dataset). |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/WACV.2018.00068 | 2018 IEEE Winter Conference on Applications of Computer Vision (WACV) |
Keywords | DocType | Volume |
deep neural networks,off-the-shelf deep architectures,specific food classification problem,deep scheme,food structure,vertical food traits,slice convolution block,deep residual blocks,Food-101 dataset,wide-slice residual networks,DNN,food composition analysis,computer vision algorithms,image-based food recognition,hand-crafted representations,classification score,benchmark datasets | Conference | abs/1612.06543 |
ISSN | ISBN | Citations |
2472-6737 | 978-1-5386-4887-2 | 3 |
PageRank | References | Authors |
0.38 | 29 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Niki Martinel | 1 | 349 | 24.39 |
| Gian Luca Foresti | 2 | 781 | 83.01 |
| C. Micheloni | 3 | 934 | 62.52 |