CuisineNet: Food Attributes Classification using Multi-scale Convolution Network
This work addresses food attribute classification for culinary analysis, but it is incremental as it builds on existing deep learning methods with a new dataset.
The paper tackled the problem of identifying food culture and flavor by classifying cuisine and flavor attributes from images, achieving 65% and 62% average F1 scores on validation and test sets, outperforming state-of-the-art models.
Diversity of food and its attributes represents the culinary habits of peoples from different countries. Thus, this paper addresses the problem of identifying food culture of people around the world and its flavor by classifying two main food attributes, cuisine and flavor. A deep learning model based on multi-scale convotuional networks is proposed for extracting more accurate features from input images. The aggregation of multi-scale convolution layers with different kernel size is also used for weighting the features results from different scales. In addition, a joint loss function based on Negative Log Likelihood (NLL) is used to fit the model probability to multi labeled classes for multi-modal classification task. Furthermore, this work provides a new dataset for food attributes, so-called Yummly48K, extracted from the popular food website, Yummly. Our model is assessed on the constructed Yummly48K dataset. The experimental results show that our proposed method yields 65% and 62% average F1 score on validation and test set which outperforming the state-of-the-art models.