Food/Non-food Image Classification and Food Categorization using Pre-Trained GoogLeNet Model

Recent past has seen a lot of developments in the field of image-based dietary assessment. Food image classification and recognition are crucial steps for dietary assessment. In the last couple of years, advancements in the deep learning and convolutional neural networks proved to be a boon for the image classification and recognition tasks, specifically for food recognition because of the wide variety of food items. In this paper, we report experiments on food/non-food classification and food recognition using a GoogLeNet model based on deep convolutional neural network. The experiments were conducted on two image datasets created by our own, where the images were collected from existing image datasets, social media, and imaging devices such as smart phone and wearable cameras. Experimental results show a high accuracy of 99.2% on the food/non-food classification and 83.6% on the food category recognition

Food Recognition using Fusion of Classifiers based on CNNs

With the arrival of convolutional neural networks, the complex problem of food recognition has experienced an important improvement in recent years. The best results have been obtained using methods based on very deep convolutional neural networks, which show that the deeper the model,the better the classification accuracy will be obtain. However, very deep neural networks may suffer from the overfitting problem. In this paper, we propose a combination of multiple classifiers based on different convolutional models that complement each other and thus, achieve an improvement in performance. The evaluation of our approach is done on two public datasets: Food-101 as a dataset with a wide variety of fine-grained dishes, and Food-11 as a dataset of high-level food categories, where our approach outperforms the independent CNN models

Recognition and Classification of Fast Food Images

Image processing is widely used for food recognition. A lot of different algorithms regarding food identification and classification have been proposed in recent research works. In this paper, we have use an easy and one of the most powerful machine learning technique from the field of deep learning to recognize and classify different categories of fast food images. We have used a pre trained Convolutional Neural Network (CNN) as a feature extractor to train an image category classifier. CNN2019;s can learn rich feature representations which often perform much better than other handcrafted features such as histogram of oriented gradients (HOG), Local binary patterns (LBP), or speeded up robust features (SURF). A multiclass linear Support Vector Machine (SVM) classifier trained with extracted CNN features is used to classify fast food images to ten different classes. After working on two different benchmark databases, we got the success rate of 99.5% which is higher than the accuracy achieved using bag of features (BoF) and SURF