Table 3:
Traditional and deep learning classification methods.
| Traditional Methods | ||||||
|---|---|---|---|---|---|---|
| Study | Approach | Database | Performance** | Reference | ||
| Features | Classifier | Top1 Acc. | Top5 Acc. | |||
| Chen, 2012 | SIFT, LBP, color and gabor | Multi-class Adaboost | Chen | 68.3% | 90.9% | [22] |
| Beijbom et al., 2015 | SIFT, LBP, color, HOG and MR8 | SVM | 77.4% | 96.2% | [20] | |
| Anthimopoulos et al., 2014 | SIFT and color | Bag of Words and SVM | Diabetes | 78.0% | - | [31] |
| Bossard et al., 2014 | SURF and L*a*b color values | RFDC | Food-101 | 50.8% | - | [18] |
| Hoashi et al., 2010 | Bag of features, color, gabor texture and HOG | MKL | Food85 | 62.5% | - | [25] |
| Beijbom et al., 2015 | SIFT, LBP, Color, HOG and MR8 | SVM | Menu-Match | 51.2%* | [20] | |
| Christodoulidis et al., 2015 | Color and LBP | SVM | Local dataset | 82.2% | - | [34] |
| Pouladzadeh et al., 2014 | Color, texture, size and shape | SVM | 92.2% | - | [12] | |
| Pouladzadeh et al., 2014 | Graph Cut, color, texture, size and shape | SVM | 95.0% | - | [12] | |
| Kawano and Yanai, 2013 | Color and SURF | SVM | - | 81.6% | [27] | |
| Farinella et al., 2014 | Bag of textons | SVM | PFID | 31.3% | - | [24] |
| Yang et al., 2010 | Pairwise local features | SVM | 78.0% | - | [28] | |
| He et al., 2014 | DCD, MDSIFT, SCD, SIFT | KNN | TADA | 64.5% | - | [29] |
| Zhu et al., 2015 | Color, texture and SIFT | KNN | 70.0% | - | [10] | |
| Matsuda et al., 2012 | SIFT, HOG, Gabor texture and color | MKL-SVM | UEC-Food-100 | 21.0% | 45.0% | [9] |
| Liu et al., 2016 | Extended HOG and Color | Fisher Vector | 59.6% | 82.9% | [36] | |
| Kawano and Yanai, 2014 | Color and HOG | Fisher Vector | 65.3% | - | [39] | |
| Yanai and Kawano, 2015 | Color and HOG | Fisher Vector | 65.3% | 86.7% | [35] | |
| Kawano and Yanai, 2014 | Fisher Vector, HOG and color | One x rest Linear classifier | UEC-Food-256 | 50.1% | 74.4% | [38] |
| Yanai et al., 2015 | Color and HOG | Fisher Vector | 52.9% | 75.5% | [35] | |
| Deep Leaning Methods | ||||||
| Study | Approach | Dataset | Topi | Top5 | Reference | |
| Anthimopoulos et al., 2014 | ANNnh | Diabetes | 75.0% | - | [31] | |
| Bossard et al., 2014 | Food-101 | Food-101 | 56.4% | - | [18] | |
| Yanai and Kawano, 2015 | DCNN-Food | 70.4% | - | [35] | ||
| Liu et al., 2016 | DeepFood | 77.4% | 93.7% | [36] | ||
| Meyers, 2015 | GoogleLeNet | 79.0% | - | [11] | ||
| Hassannejad et al., 2016 | Inception v3 | 88.3% | 96.9% | [32] | ||
| Meyers, 2015 | GoogleLeNet | Food201 segmented | 76.0% | - | [11] | |
| Menu-Match | 81.4%* | - | ||||
| Christodoulidis et al., 2015 | Patch-wise CNN | Own database | 84.90% | - | [34] | |
| Pouladzadeh et al., 2016 | Graph cut+Deep Neural Network | 99.0% | - | [40] | ||
| Kawano and Yanai, 2014 | OverFeat+Fisher Vector | UEC-Food-100 | 72.3% | 92.0% | [39] | |
| Liu et al., 2016 | DeepFood | 76.3% | 94.6% | [36] | ||
| Yanai and Kawano, 2015 | DCNN-Food | 78.8% | 95.2% | [35] | ||
| Hassannejad et al., 2016 | Inception v3 | 81.5% | 97.3% | [32] | ||
| Chen and Ngo, 2016 | Arch-D | 82.1% | 97.3% | [23] | ||
| Liu et al., 2016 | DeepFood | UEC-Food-256 | 54.7% | 81.5% | [36] | |
| Yanai and Kawano, 2015 | DCNN-Food | 67.6% | 89.0% | [35] | ||
| Hassannejad et al., 2016 | Inception v3 | 76.2% | 92.6% | [32] | ||
| Ciocca et al., 2016 | VGG | UNIMINB2016 | 78.3% | - | [8] | |
| Chen and Ngo, 2016 | Arch-D | VIREO | 82.1% | 95.9% | [23] | |
*
Represents the mean average precision
**
Top 1 and/or Top 5 indicate that the performance of the classification model was evaluated based on the first assigned class with the highest probability and/or the top 5 classes among the prediction for each given food item, respectively