An Automatic Calorie Estimation System of Food Images
on a Smartphone
Koichi Okamoto

Keiji Yanai

Department of Informatics,
The University of Electro-Communications, Tokyo
1-5-1 Chofugaoka, Chofu, Tokyo 182-8585 JAPAN{okamoto-k,yanai}@mm.inf.uec.ac.jp
ABSTRACTIn recent years, due to a rise in healthy thinking on eating, many people take care of their eating habits, and somepeople record daily diet regularly. To assist them, many
mobile applications for recording everyday meals have beenreleased so far. Some of them employ food image recognition which can estimate not only food names but also foodcalorie. However, most of such applications have some problems especially on their usability. Then, in this paper, we
propose a novel single-image-based food calorie estimation
system which runs on a smartphone as a standalone application without external recognition servers. The proposedsystem carries out food region segmentation, food region
categorization, and calorie estimation automatically. By theexperiments and the user study on the proposed system, theeffectiveness of the proposed system was confirmed.

1. INTRODUCTION
In recent years, due to a rise in healthy thinking on eating, many people take care of their eating habits, and somepeople record daily diet regularly. To assist them, many
mobile applications for recording everyday meals have beenreleased so far. Some of them employ food image recognition which can estimate not only food names but also foodcalories. However, in most of the cases, the estimated calories are just associated with the estimated food categories,or the relative size compared to the standard size of eachfood category which are usually provided by a user manually. Most of the applications does not estimate calories
based on the amount of foods.
Then, in this work, we try to implement a mobile application which can estimate more accurate calories by simply
taking one meal photo. The proposed application employs
food region segmentation as well as food category recogniPermission to make digital or hard copies of all or part of this work for personal orclassroom use is granted without fee provided that copies are not made or distributedfor profit or commercial advantage and that copies bear this notice and the full citationon the first page. Copyrights for components of this work owned by others than theauthor(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or
republish, to post on servers or to redistribute to lists, requires prior specific permission
and/or a fee. Request permissions from permissions@acm.org.

MADiMa’16, October 16 2016, Amsterdam, Netherlandsc 2016 Copyright held by the owner/author(s). Publication rights licensed to ACM.
ISBN 978-1-4503-4520-0/16/10. . . $15.00
DOI: http://dx.doi.org/10.1145/2986035.2986040

tion for a given meal photo.
In this work, we propose a mobile application for foodcalorie estimation from a signal photo which runs on Android smartphone. Taking into account usability and mobility of a system, we implement the system as a stand-alone
mobile application which can be used even in an airplane
or in the underground where the Internet connection is notavailable.
To estimate food calorie from a single image, a user needs
to register a size-known reference object in advance and to
take a food photo with the registered reference object. As
a reference object, we assume a personal belonging which
we are always carrying such as a wallet and a credit-cardsize card in a wallet (except a smartphone for taking a meal
photo). After taking a meal photo with a reference object,
the system carries out segmentation of food items and the
pre-registered reference object. Because the real size of thereference object is known (e.g. In case of a credit-card-size
object, the size is 85.6mm x 54mm.), the system can estimate the real size of each detected food items. By using theestimated real size and the equations to calculate food calorie from their size, the system finally estimates the calorie ofthe food items in the real photo. Figure 1 shows an example
of usage and a screen-shot of the proposed system.
In the rest of this paper, we describe related work in Section 2. We explain an overview of the proposed mobile system in Section 3 and the detail of the methods in Section 4.
In Section 5, we report the experimental results including
evaluation of food calorie estimation and user study on the
proposed system. Finally we discuss the proposed systemand the experimental results in Section 6, and conclude thispaper in Section 7.

2.

RELATED WORK

Various systems on automatic food calorie estimation havebeen proposed so far. We review them in this section.
Miyazaki et al. [1] proposed an image-search-based method
to estimate food calories. They prepared a large number offood photos annotated with calorie values by dietitians, andthey searched for the similar food photos to a given meal
photo from their database. Although example-based calorie estimation was a novel approach, they did just searchfor similar looking images and transfer the annotated calo-Figure 1: (Left) A user is taking a food photo with the pre-registered reference object (a used pre-paid card).
(Right) A screen-shotrie values. Therefore the estimation accuracy was not high.
Most of the other works performed 3D volume estimation orat least 2D size estimation for accurate food calorie estimation.
Chen et al. [2] proposed an image-based food calorie estimation method using RGB-D images captured by depth
cameras such as Kinect. Since a depth camera is not common and consumer smartphones have no depth cameras, it
was difficult to implement the method on mobile devices.
Kong et al. [3] proposed a mobile application to estimate
food calories from multiple images, “DietCam”. They carriedout segmentation and food item recognition, and in addition
reconstructed 3D volumes of food items and calculate foodcalories based estimated volumes. 3D reconstruction was
performed with SIFT-based keypoint matching and homography estimation which were a standard method of 3D stereo
vision. They supported recognition and calorie estimation of
leftover foods as well. Their system were a fully client-sidesystem. Our work is similar to this work except for using
only single images. In addition, we use deep-learning-based
food recognition.
Xu [4] employed 3D model based food volume estimationfrom a single food image. They classify food items into some
types of representative shapes such as a cylinder and a box,
and they fitted corresponding 3D models to food items for
estimating food volumes. They claimed that 3D model fitting was effective for most of the food items but not for somefoods such as vegetables and breads. They also examined
multiple view food volume estimation using the shape-fromsilhouettes methods, which required many different-view images (e.g. 10 images).
Pouladzadhe et al. [5] proposed a food calorie estimation
system which needed two dish images taken from the top andthe side and used a thumb of a user as a reference object.
Their method to estimate volumes were calculated by multiplying the size of food items estimated from the top-view
image by the height estimated from the side-view image,which was relatively a straight-forward way. Different fromtheir work, in our work, we assume that the height of foodportion correlates with the size of foods and food categories,
and we estimate calories of food items directly from the foodsizes estimated from the top-view image. In our system, asa reference object, we use a personal belonging which we arealways carrying such as a credit-card-size card or a wallet,
and we use only a single mean photo taken from the top
with a reference object.
Although it is reasonable to use multiple images [3, 5] toestimate food volumes more accurately which is sometimes
troublesome, we prioritize usability as a mobile application
rather than accurate estimation of food calories.
Myers et al. [6] proposed “Im2Calories” which was a foodcalorie estimation application for Android smartphones. Although this projects were carried out by the Google Researchand they made the press-release on this project about one
year ago, Android applications as well as the dataset which
they announced they would make to the public in their paper
have not been released yet. They employed state-of-the-art
CNN-based segmentation [7] and CNN-based 3D volume estimation from 2D single images [8] in addition to CNN-based
food category recognition. However, the experiments described in the paper was small-scale, which seemed far frompractical use for common consumers.
All the above-mentioned systems employ food dish recognition to estimate food calorie. On the other hand, Okamoto
et al. proposed GrillCam [9] which monitored eating actions of a user continuously. When a user conveys foods to
his/her mouth, the system detects the moment, recognizes
food categories and estimates rough volumes and rough calorie intake from the video stream. The estimated food calorie
intake are accumulated during the meal, and after finishing
the meal the user can get to know categories of eaten fooditems, their amounts and the total amount of calorie intake.
Although this is based on the very different approach fromthe other works, it has drawback that the estimation biases
tend to be accumulated.

3.

OVERVIEW OF THE SYSTEM

In this paper, we propose a food calorie estimation systemrunning on a consumer smartphone. In the proposed system,
a user needs to take only a single meal photo from the top(right overhead) with a pre-registered reference object. After taking photo, the system segments out food items, classify them into the pre-defined food categories, and finallyestimates food calories of each of the detected food items.

The processing steps of the proposed system are as follows(see Figure 2 as well):4.

1. Take a meal photo from the top with a reference object.

4.1

2. Extract regions of food items and the reference object.

In this subsection, we describe the method to extract foodregions. Firstly, we estimate rough location of dishes by edge
detection, secondly we extract a bounding box of a foodregion by color-pixel-based k-means clustering, and finally
we apply GrabCut [10] using the estimated bounding box toobtain an accurate food region. Figure 3 shows the flow ofthese processing.

3. Recognize food categories of the detected food items.
4. Calculate the real sizes of the foods and food calories
based on the pre-trained relation between sizes andcalories.
In our mobile system, we assume that a meal photo istaken from the overhead, which enables us to omit correction of trapezoidal distortion. We assume that backgroundof food dishes are not textured but uniform for making segmentation easy. In addition, we also assume that the size ofthe reference object is known. In fact, in the application we
implemented, a user can register the size of a reference object which is expected to the user’s personal belonging suchas a credit-card-size card in the wallet or a wallet. Note that
we do not recommend to use a credit card as a reference object for the security reason. Instead we recommend to use
a used pre-paid card such as a pre-paid card for a pre-paid
mobile phone, Internet shopping, and Internet games.
To extract regions of food items and a reference object,
firstly we estimate rough position of dishes based on edge
detection results, and secondly we apply color-pixel-based
k-means clustering for estimating bounding box of food regions. Finally we apply GrabCut [10] with the detectedbounding box as a segmentation seed.
For food classification, we use a Convolutional Neural Network (CNN) based recognition engine which run on a consumer smartphone [11] with high accuracy. It takes only 0.2
seconds on a consumer Android phone.

DETAIL OF THE METHODSIn this section, we explain the detail of each step.

Food Region Segmentation

Figure 3: The processing steps of food region extraction.

4.1.1

Dish Region Detection

In the first, we detect dish regions from a given meal
photo. We apply edge detection to a given image afterslight smoothing, and extract bounding boxes which surrounds edges as shown in Figure 4. Note that we expectthat all the dishes and the reference object are separated
and do not touch with each other. After this step, all theprocessing are applied to each bounding box detected in thisstep.
By applying the GrabCut method with this bounding box,
we can extract dish regions correctly as shown in the rightside of Figure 5. Without the bounding box detected byedges, we obtain incomplete regions as shown in the left
side of Figure 5.

4.1.2

Figure 2: The flow of the proposed method.

Food Region Detection

In the previous step, we extracted not food regions but
dish regions. As the next step, we need to detect food regions
which are expected inside the detected dish region for foodcalorie estimation. To do this, we adopt two step processing consisting of rough segmentation by color-pixel-based
k-means clustering and GrabCut.

Figure 4: An example of the detected result of thebounding box surrounding edges.

Figure 6: An example of a segmentation result by
k-means. (Skyblue: food region, Blue: dish region,
White: background)tracted. Accurateness of food region extraction is importantfor image-based food calorie estimation.

Figure 5: (Left) GrabCut results without the bounding box. (Right) GrabCut results using the detecteddish bounding box.

Although a graph-based segmentation method, GrabCut [10],is a highly effective method to extract a specific object, itneeds to be given a bounding box which shows rough location of the target object. Since we aim at automatic calorie
estimation, we need to estimate a bounding box by othermethods before applying GrabCut. To do that, we adopt
a simple region segmentation method which is based on kmeans clustering [12].
We simply perform k-means with k = 3 for all the pixels within the target dish bounding box, which means we
expected all the pixels into three kinds of the regions consisting of “foods”, “dish”, and “background”. Note that weapply median smoothing before applying k-means in orderto prevent small noise regions from being generated. We
assume the order of the regions from the center to the outside are always “foods”, “dish”, and “background”. Figure 6
shows an example of the segmentation result by k-means.

4.1.3 Segmentation with GrabCut
In the previous step, we obtained a food region by kmeans. Since this is a simple method, the result tends to
be incomplete. Therefore, for more accurate estimation, we
adopt GrabCut [10] which is graph-based region segmentation and employs energy minimization based on graph cut.
Since it was originally proposed as an interactive region segmentation method, minimum supervision is needed. Here,as a supervision signal, we use a bounding box which indicates that the target object is inside it.
We estimate the bounding box inscribed with the detectedfood region as a seed of GrabCut, and apply GrabCut withthis seed. We can expect more accurate food region is ex-

Figure 7: (Right) The final food region estimatedby the three-step processing. (Left) Original meal
photo.

4.2

Extraction of a Reference Object

To estimate a real size of a food region, we assume that asize-known reference object is taken in a meal photo which
we like to estimate food calories at the same time. In thiswork, we assume that a meal photo is taken from the overhead, which enables us to omit correction of trapezoidal distortion. Therefore, we can use not only a rectangular object
such as a card but also any shaped objects such as a wallet
and a smartphone case.
To enable us to extract any shaped objects from an image,
we use GrabCut in the same way as food region extraction.
Figure 8 shows a wallet as an example of a reference object,
and the result of the detected region. In the current implementation, a reference object is assumed to be located in
the left of a given photo.

4.3

Food Category Recognition

The proposed system employs a Convolutional Neural Network based food recognition engine which completes all theprocessing inside a smart phone and does not needs to communicate with external recognition servers.
The proposed CNN-based food recognition engine employs “Network in Network (NIN)” [13]. As basic CNN architectures for object recognition, AlexNet [14], Networkin-Network (NIN) [13], GoogLeNet [15] and VGG-net [16]

by the following equation:
Fr = Sr ∗Figure 8: (Left) An example of a reference object,wallet. (Right) The extracted region of the wallet.

are commonly used. Because both AlexNet and VGG-net
have fully-connected layers which requires a huge number ofweight parameters, 59M for AlexNet and 129M for VGG16, they are not appropriate for mobile implementation. Although both GoogLeNet and NIN has around 7M parameters, GoogLeNet is quite complicated which consists of many
“Inception modules” each of which consists of 5x5, 3x3, 1x1
conv layers and pooling layers. With these reason, we selected NIN as an architecture for mobile food recognition.
Regarding mobile implementation on CNN-based recognition engine, we followed the work on efficient mobile implementation [11].
We trained the NIN with the ILSVRC 2012 dataset consisting of one million images with 1000 categories from scratch,and fine-tuned it with the UEC-FOOD100 dataset [17] using
the Caffe framework [18]. The recognition engine can recognize a 227x227 image with 250ms on SAMSUNG Galaxy
S5 (QuadCore 2.5GHz). The recognition accuracy was much
improved compared to non-CNN-based mobile food recognition. Our CNN-based engine achieved 75.0% and 93.7% for
the top-1 and top-5 accuracy, respectively, while non-CNN
based “FoodCam” [19] achieved 65.3% and 86.7%.

4.4 Calorie Estimation
Although several existing works on food calorie estimation [3, 4, 5] carried out 3D reconstruction of food shapes
for estimating calories, they required multiple-view images
to do that. On the other hand, we use a single-view image
for calorie estimation. Being different from the prior works,
in our work, we assume that the height of food portion correlates with the size of foods and food categories, and weestimate calories of food items directly from the food sizes
estimated from the top-view image. To do that, we use notsimple linear estimation but quadratic curve estimation fromthe 2D size of foods to their calories. The quadratic curve ofeach food category is estimated based on the training data
annotated with real food calories independently.
The real size of food regions is estimated based on thesize of the reference object. Since the real size of reference
object is known, the real size of foods, Fr , can be obtained

Fp
,
Sp

(1)

where Fp represents the number of pixels of the region of
the target food item, Sp represents the number of pixels of
the region of the reference object, and Sr represents the real
size of the top view of the reference object which is expected
to be registered in advance.
After calculating the real size of the target food item, weestimate its calorie value. To do that, we need to convert
the 2D top-view size of the food to the calorie value. For
the planar-shaped foods such as a stake and a hot-cake, we
can assume that food calorie is proportional to the size ofa food item. However, for the foods the shape of which are
3-D such as rice bowl and miso soup, this assumption does
not fit. Therefore, in this work, we use not simple linear
estimation but quadratic curve estimation from the 2D topview size of foods to their calories. The quadratic curve ofeach food category is estimated based on the training data
annotated with real food calories independently. The calorie
value is approximately calculated by the following equation:
C = ai ∗ Fr2 + bi ∗ Fr + ci ,(2)where ai , bi , ci are constant values for the i-th food category which are trained from the calorie-annotated food image dataset in advance. In the experiments, we estimatedai , bi , ci using a polynomial curve fitting method in a leastsquares sense.

5.

EXPERIMENTS

5.1

Food Dataset Annotated with Calorie Values

To make experiments for image-based food calorie estimation, at first, we prepared a food image dataset in which
each image was annotated with a food calorie value.
To create a calorie-known dataset, we bought 20 kinds ofJapanese foods sold in the packages indicating food calories
at supermarkets, convenience stores and lunch box shops.
We prepared two packages for each food category, because
we needed to create a training dataset and a test dataset independently. We dish up them in three kinds of the amounts(large, medium, small) on plates or bowls, and took foodphotos with uniform backgrounds. Figure 9 and 10 shows
three size dishes of “croquette” and “meat and potato mix”(“Nikujaga” in Japanese). The amounts of foods to place ondishes were measured by a kitchen scale. We assumed a foodcalorie was proportional to the weight of a food, and calculated food calories of three different amounts of each food
item according to its weight and the total calorie shown on
the package. Totally we collected totally 120 food images
containing 20 categories with three different amounts. Halfof 120 images were used for training of Equation 2 (estimating ai , bi , ci ), and the rest were used for evaluation.

Figure 9: Croquette. (Left: small, Middle: medium, Right: large)

Figure 10: Meat and potato mix (Japanese food “Nikujaga”). (Left: small, Middle: medium, Right: large)

5.2 Evaluation on Calorie Estimation
We made experiments for evaluating the proposed methodof image-based calorie estimation using 60 food images in thetest dataset. Table 1 shows the average absolute errors, therelative average absolute errors and standard deviations of
all the estimated results for 60 test images.
Table 1: Absolute and relative average errors andstandard deviations (s.d.) of all the estimated results.
avg. absolute err. abs. s.d. relative avg. err. rel. s.d.
52.23140.401
0.213
0.823
In the experiments for 20 kinds of the target foods, theabsolute error was 50kcal, and the relative error was 20%,
which were promising results.

5.3 User Study
We made user study on calorie estimation on a real setting and the usability of the proposed system as well. As abaseline system, we used a mobile food recognition application, “FoodCam”, proposed by Kawano et al. [19]. Although“FoodCam” can recognize food categories, it cannot estimate
the amount of foods. Instead, on “FoodCam” we can adjust
the amount of the target foods by using the slider on the GUI
manually, and get to know the food calorie corresponding to
the given amount.
We asked 12 subjects who had no special knowledge about
nutrition to use both our system and the baseline system
to estimate calories for three kinds of actual foods. We

used “beef rice bowl”, “croquette”, and “salad” as target fooditems. We evaluated errors between estimated calories andreal calories and compared the results by two systems. Table 2 shows errors and standard deviations on the results byboth the systems.
Although the average error of the estimated calorie of“beef rice bowl” by the propose system became larger thanthe baseline, regarding “croquette” and “salad” the proposedsystem reduced the average error greatly compared to thebaseline.
In addition, we asked the subjects to evaluate usabilityof both systems in five step evaluation. The larger valuesmeans the better evaluation. Table 3 shows the results whichmeans that the larger value is the better evaluation.
Table 3: Five-step evaluation on usability for recording meals with calorie information by 12 subjects.
How easy to take meal records
Baseline
2.83±0.80
Proposed
4.25±0.72
As results, the proposed system was evaluated as mucheasier to use with a large margin than the baseline system.
This is partly because our system can estimate food calories
automatically simply by taking a meal photo from the top,while the baseline needs to adjust a slider which indicates
relative amounts of the target foods.

Table 2: Estimation errors by the baseline system, “FoodCam” and the proposed system.
food namebeef rice bowl
croquette
saladreal value (kcal)
962
552
14

baseline
avg. err.
SD-53.25 209.79
-242
91.26
54.83
36.28

6. DISCUSSION
In the experiments, the absolute average error was 52.231kcal,
and the relative average error was 21.3%. This 21.3% error
was not a bad result, because the ministry of consumer in
the Japanese Government defines 20% error as an allowableerror on calorie values shown in the food packages. Basicallycalorie estimation by looking is difficult task even for experienced dietitians. Therefore, 20% error can be regarded aspromising, allowable, and probably useful from the practicalpoint of view.
In this work, we have prepared only 120 calorie-annotatedfood photos. This is not enough. However, it is not easy to
prepare calorie-annotated food photos, although there areso many food photos on Web which have no information on
calorie. To build such dataset, we have to prepare real foods
and take their photos, which is extremely difficult to do thisin the large-scale way. Although Google announced theywould release a calorie-annotated food photo dataset in theirpaper [6], they have not released such dataset yet half year
after publishing the paper. (It is completely unacceptablethat they cheated both reviewers and readers.) It is thebiggest issue how to build a large-scale calorie-annotatedfood photo dataset.

7. CONCLUSIONSIn this paper, we proposed an image-based calorie estimation system which runs on a consumer smartphone without
external recognition servers. The system estimate food calories automatically by simply taking a meal photo from the
top with a pre-registered reference object.
In the step to detect food regions, we used edge-baseddish localization and k-means-based dish bounding box estimation, and finally we applied GrabCut for accurate foodregion estimation. To recognize each of the detected foodregions, we adopted the CNN-based food recognition engine which recognizes food photos in around 0.2 seconds.
To estimate calorie, we used not simple linear estimation
but quadratic curve estimation from the 2D size of foods totheir calories. The quadratic curve of each food category is
trained based on the training data annotated with real foodcalories independently. As results, in the experiments, weachieved the absolute average error, 52.231kcal, and the relative average error, 21.3%, regarding food calorie estimation
with 60 test images annotated with real food calorie values.
In addition, we obtained positive evaluation by the subjects
regarding the usability of the proposed system compared tothe baseline system.
In this work, we adopted relatively simple segmentation

proposed
avg. err.
SD-242 55.10-47.08 52.52
4.86 11.87

methods because of taking into account mobile implementation. Therefore, it is sometime difficult to treat a meal photowith non-uniform background shown as Figure 11. For future work, we like to introduce more sophisticated segmentation methods, hopefully state-of-the-art CNN-based methods such as [20]. In addition, we like to introduce planarcalibration by using a rectangular card as a reference object, which relaxes the assumption of taking a meal photofrom the overhead.

Figure 11: A meal photo in the real setting with
non-uniform background.

8.

REFERENCES[1] T. Miyazaki, De. S. G. Chamin, and K. Aizawa,“Image-based calorie content estimation for dietaryassessment,” in IEEE International Symposium on
Multimedia, pp. 363–368, 2011.
[2] M. Chen, Y. Yang, C. Ho, S. Wang, S. Liu, E. Chang,
C. Yeh, and M. Ouhyoung, “Automatic chinese food
identification and quantity estimation,” in Proc. of
SIGGRAPH Asia Technical Briefs, p. 29, 2012.
[3] F. Kong and J. Tan, “Dietcam: Automatic dietaryassessment with mobile camera phones,” in Proc. of
Pervasive and Mobile Computing, pp. 147–163, 2012.
[4] C. Xu, Y. He, N. Khannan, A. Parra, C. Boushey, and
E. Delp, “Image-based food volume estimation,” inProceedings of the international workshop on
Multimedia for cooking & eating activities, pp. 75–80,
2013.
[5] P. Pouladzadeh, S. Shirmohammadi, and
R. Almaghrabi, “Measuring calorie and nutrition fromfood image,” IEEE Transactions on Instrumentationand Measurement, pp. 1947–1956, 2014.

[6] A. Meyers, N. Johnston, V. Rathod, A. Korattikara,
A. Gorban, N. Silberman, S. Guadarrama,
G. Papandreou, J. Huang, and K. P. Murphy,“Im2Calories: Towards an automated mobile vision
food diary,” in Proc. of IEEE International Conference
on Computer Vision (ICCV), 2015.
[7] L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy,
and A. L. Yuille, “Semantic image segmentation withdeep convolutional nets and fully connected CRFs,”
2014.
[8] D. Eigen and R. Fergus, “Predicting depth, surface
normals and semantic labels with a common
multi-scale convolutional architecture,” in Proc. ofIEEE International Conference on Computer Vision(ICCV), pp. 2650–2658, 2015.
[9] K. Okamoto and K. Yanai, “Grillcam: A real-timeeating action recognition system,” in Proc. ofInternational Conference on Multimedia Modelling(MMM), 2016.
[10] C. Rother, V. Kolmogorov, and A. Blake, “GrabCut:
Interactive foreground extraction using iterated graphcuts,” in Proc. of ACM SIGGRAPH, pp. 309–314,
2004.
[11] K. Yanai, R. Tanno, and K. Okamoto, “Efficientmobile implementation of a cnn-based object
recognition system,” in Proc. of the ACM
International Conference on Multimedia, 2016.
[12] J. A. Hartigan and M. A. Wong, “A K-meansclustering algorithm,” Applied Statistics, vol. 28, pp.
100–108, 1979.

[13] M. Lin, Q. Chen, and S. Yan, “Network in network,”
In Proc. of International Conference on Learning
Represenation Conference Track, 2013.
[14] A. Krizhevsky, S. Ilya, and G. E. Hinton, “Imagenet
classification with deep convolutional neuralnetworks,” in Advances in Neural Information
Processing Systems, pp. 1097–1105. 2012.
[15] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed,
D. Anguelov, D. Erhan, V. Vanhoucke, and
A. Rabinovich, “Going deeper with convolutions,” inProc. of IEEE Computer Vision and Pattern
Recognition, pp. 1–9, 2015.
[16] K. Simonyan and A. Zisserman, “Very deep
convolutional networks for large-scale image
recognition,” 2015.
[17] “UEC FOOD 100,”
http://foodcam.mobi/dataset.html.
[18] Y. Jia, E. Shelhamer, J. Donahue, S. Karayev,
J. Long, R. Girshick, S. Guadarrama, and T. Darrell,“Caffe: Convolutional architecture for fast featureembedding,” in Proc.of ACM International Conference
Multimedia, pp. 675–678, 2014.
[19] Y. Kawano and K. Yanai, “FoodCam: A real-time
food recognition system on a smartphone,” Multimedia
Tools and Applications, vol. 74, no. 14, pp. 5263–5287,
2015.
[20] W. Shimoda and K. Yanai, “CNN-based food image
segmentation,” in Proc. of International Workshop on
Multimedia Assisted Dietary Management(MADIMA), 2015.