A Real-time Junk Food Recognition System based on Machine Learning

  title={A Real-time Junk Food Recognition System based on Machine Learning},
  author={Sirajum Munira Shifat and Takitazwar Parthib and Sabikunnahar Talukder Pyaasa and Nila Maitra Chaity and N. Udhaya Kumar and Md. Kishor Morol},
As a result of bad eating habits, humanity may be destroyed. People are constantly on the lookout for tasty foods, with junk foods being the most common source. As a consequence, our eating patterns are shifting, and we’re gravitating toward junk food more than ever, which is bad for our health and increases our risk of acquiring health problems. Machine learning principles are applied in every aspect of our lives, and one of them is object recognition via image processing. However, because… 

Figures from this paper


A machine learning approach to recognize junk food
This study tried to recognize local junk foods based on a new dataset consist of 2000 data belonging 5 junk food classes which was believed to be unique in every sense and Convolution Neural Network technology was used to reach the goal.
Im2Calories: Towards an Automated Mobile Vision Food Diary
A system which can recognize the contents of your meal from a single image, and then predict its nutritional contents, such as calories, is presented, significantly outperforming previous work.
Food Recognition: A New Dataset, Experiments, and Results
A new dataset for the evaluation of food recognition algorithms that can be used in dietary monitoring applications by designing an automatic tray analysis pipeline that takes a tray image as input, finds the regions of interest, and predicts for each region the corresponding food class.
A Food Recognition System for Diabetic Patients Based on an Optimized Bag-of-Features Model
The proposed methodology for automatic food recognition, based on the bag-of-features (BoF) model, achieved classification accuracy of the order of 78%, thus proving the feasibility of the proposed approach in a very challenging image dataset.
Food Detection and Recognition Using Convolutional Neural Network
This paper constructed a dataset of the most frequent food items in a publicly available food-logging system, and used it to evaluate recognition performance, and found that the convolution kernels show that color dominates the feature extraction process.
Yelp Food Identification via Image Feature Extraction and Classification
This paper uses image pre-processing techniques, including filtering and image augmentation, feature extraction via convolutional neural networks (CNN), and three ways of classification algorithms to identify up to ten kinds of food via raw photos from the challenge dataset.
A Deep Convolutional Neural Network for Food Detection and Recognition
  • M. Subhi, S. M. Md Ali
  • Computer Science
    2018 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES)
  • 2018
A new deep convolutional neural network configuration is proposed to detect and recognize local food images and it was found out that convolution masks show that the features of food color dominate the features map.
Support Vector Machine and YOLO for a Mobile Food Grading System
FoodTracker: A Real-time Food Detection Mobile Application by Deep Convolutional Neural Networks
A deep convolutional neural network merging with YOLO is built to achieve simultaneous multi-object recognition and localization with nearly 80% mean average precision and is well-suited for mobile devices with negligible inference time and small memory requirements with a deep learning algorithm.
Food image recognition using deep convolutional network with pre-training and fine-tuning
  • Keiji Yanai, Y. Kawano
  • Computer Science
    2015 IEEE International Conference on Multimedia & Expo Workshops (ICMEW)
  • 2015
The fine-tuned DCNN which was pre-trained with 2000 categories in the ImageNet including 1000 food-related categories was the best method, and it was found that DCNN was very suitable for large-scale image data, since it takes only 0.03 seconds to classify one food photo with GPU.