Fine-Tuning VGG Neural Network For Fine-grained State Recognition of Food Images
This work addresses food state recognition for computer vision applications, but it is incremental as it applies an existing fine-tuning method to a new domain-specific dataset.
The paper tackled the problem of fine-grained state recognition of food images using a convolutional neural network (CNN) fine-tuned from ImageNet, achieving evidence of its effectiveness even with a small dataset of 5978 images across seven categories.
State recognition of food images can be considered as one of the promising applications of object recognition and fine-grained image classification in computer vision. In this paper, evidence is provided for the power of convolutional neural network (CNN) for food state recognition, even with a small data set. In this study, we fine-tuned a CNN initially trained on a large natural image recognition dataset (Imagenet ILSVRC) and transferred the learned feature representations to the food state recognition task. A small-scale dataset consisting of 5978 images of seven categories was constructed and annotated manually. Data augmentation was applied to increase the size of the data.