Recognition and Classification of Fast Food Images

Abstract

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 CNN 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