Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Computer vision-based food calorie estimation: dataset, method, and
  experiment   Computer vision has been introduced to estimate calories from food images.
But current food image data sets don't contain volume and mass records of
foods, which leads to an incomplete calorie estimation. In this paper, we
present a novel food image data set with volume and mass records of foods, and
a deep learning method for food detection, to make a complete calorie
estimation. Our data set includes 2978 images, and every image contains
corresponding each food's annotation, volume and mass records, as well as a
certain calibration reference. To estimate calorie of food in the proposed data
set, a deep learning method using Faster R-CNN first is put forward to detect
the food. And the experiment results show our method is effective to estimate
calories and our data set contains adequate information for calorie estimation.
Our data set is the first released food image data set which can be used to
evaluate computer vision-based calorie estimation methods.

28
The categories of this paper are: alg-geom
11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
12
The categories of this paper are: nucl-th
27
The categories of this paper are: comp-gas
7
The categories of this paper are: hep-th
15
The categories of this paper are: nucl-ex
23
The categories of this paper are: solv-int
21
The categories of this paper are: adap-org
32
The categories of this paper are: atom-ph
6
The categories of this paper are: astro-ph
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
31
The categories of this paper are: ao-sci
5
The categories of this paper are: gr-qc
3
The categories of this paper are: physics