8 research outputs found
FoodNet: Recognizing Foods Using Ensemble of Deep Networks
In this work we propose a methodology for an automatic food classification
system which recognizes the contents of the meal from the images of the food.
We developed a multi-layered deep convolutional neural network (CNN)
architecture that takes advantages of the features from other deep networks and
improves the efficiency. Numerous classical handcrafted features and approaches
are explored, among which CNNs are chosen as the best performing features.
Networks are trained and fine-tuned using preprocessed images and the filter
outputs are fused to achieve higher accuracy. Experimental results on the
largest real-world food recognition database ETH Food-101 and newly contributed
Indian food image database demonstrate the effectiveness of the proposed
methodology as compared to many other benchmark deep learned CNN frameworks.Comment: 5 pages, 3 figures, 3 tables, IEEE Signal Processing Letter
Food-101 – mining discriminative components with random forests
Bossard L., Guillaumin M., Van Gool L., ''Food-101 – mining discriminative components with random forests'', Lecture notes in computer science, vol. 8694, pp. 446-461, 2014 (13th European conference on computer vision - ECCV 2014, September 6-12, 2014, Zurich, Switzerland).status: publishe
From Plate to Prevention: A Dietary Nutrient-aided Platform for Health Promotion in Singapore
Singapore has been striving to improve the provision of healthcare services
to her people. In this course, the government has taken note of the deficiency
in regulating and supervising people's nutrient intake, which is identified as
a contributing factor to the development of chronic diseases. Consequently,
this issue has garnered significant attention. In this paper, we share our
experience in addressing this issue and attaining medical-grade nutrient intake
information to benefit Singaporeans in different aspects. To this end, we
develop the FoodSG platform to incubate diverse healthcare-oriented
applications as a service in Singapore, taking into account their shared
requirements. We further identify the profound meaning of localized food
datasets and systematically clean and curate a localized Singaporean food
dataset FoodSG-233. To overcome the hurdle in recognition performance brought
by Singaporean multifarious food dishes, we propose to integrate supervised
contrastive learning into our food recognition model FoodSG-SCL for the
intrinsic capability to mine hard positive/negative samples and therefore boost
the accuracy. Through a comprehensive evaluation, we present performance
results of the proposed model and insights on food-related healthcare
applications. The FoodSG-233 dataset has been released in
https://foodlg.comp.nus.edu.sg/