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Concurrent Food Localization and Recognition using Deep Convolution Neural Network
Rohit Thakur(로힛 타쿠르),Jusung Kang(강주성),Hyunjun Han(한현준),Heung-No Lee(이흥노) 대한전자공학회 2017 대한전자공학회 학술대회 Vol.2017 No.6
The analysis of food quality is considered as an important task because of the people’s nutrition habits that affect the global population. For this, it is very important to keep track of our eating habits by making automatic nutrition diaries. Keeping this in mind, we demonstrate how a deep learning technique is used to the tasks of localizing and recognizing various types of foods present in given images. Firstly, the Grad-CAM algorithm (a class-discriminative localization technique) along with data processing is used for generating discriminative image regions which helps in obtaining object proposals for detection purpose. Secondly, a classifier for object recognition is employed over it. Further, we compare our result with best state of art work and show our proposed method to be well equipped in terms of precision and accuracy. Through experiments we have verified that proposed approach is a convincing solution for concurrent food localization and recognition.