Name
Title | ||
|---|---|---|
DeepFood: Automatic Multi-Class Classification of Food Ingredients Using Deep Learning |
Abstract | ||
|---|---|---|
Deep learning has brought a series of breakthroughs in image processing. Specifically, there are significant improvements in the application of food image classification using deep learning techniques. However, very little work has been studied for the classification of food ingredients. Therefore, this paper proposes a new framework, called DeepFood which not only extracts rich and effective features from a dataset of food ingredient images using deep learning but also improves the average accuracy of multi-class classification by applying advanced machine learning techniques. First, a set of transfer learning algorithms based on Convolutional Neural Networks (CNNs) are leveraged for deep feature extraction. Then, a multi-class classification algorithm is exploited based on the performance of the classifiers on each deep feature set. The DeepFood framework is evaluated on a multi-class dataset that includes 41 classes of food ingredients and 100 images for each class. Experimental results illustrate the effectiveness of the DeepFood framework for multi-class classification of food ingredients. This model that integrates ResNet deep feature sets, Information Gain (IG) feature selection, and the SMO classifier has shown its supremacy for foodingredients recognition compared to several existing work in this area. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/CIC.2017.00033 | 2017 IEEE 3rd International Conference on Collaboration and Internet Computing (CIC) |
Keywords | Field | DocType |
Image classification,Food recognition,Multi class classification,Deep learning,Feature extraction,Convolutional neural network | Computer vision,Feature selection,Convolutional neural network,Computer science,Transfer of learning,Feature extraction,Artificial intelligence,Deep learning,Contextual image classification,Classifier (linguistics),Machine learning,Multiclass classification | Conference |
ISBN | Citations | PageRank |
978-1-5386-2566-8 | 1 | 0.35 |
References | Authors | |
35 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lili Pan | 1 | 4 | 2.79 |
| Samira Pouyanfar | 2 | 141 | 13.06 |
| Hao Chen | 3 | 156 | 61.18 |
| Jiaohua Qin | 4 | 27 | 10.01 |
| Shu-Ching Chen | 5 | 1978 | 182.74 |