Twitter Food Photo Mining and Analysis
for One Hundred Kinds of Foods
Keiji Yanai and Yoshiyuki Kawano
Department of Informatics, The University of Electro-Communications
1-5-1 Chofugaoka, Chofu-shi, Tokyo 182-8585 Japan{yanai,kawano-y}@mm.inf.uec.ac.jp
Abstract. So many people post photos as well as short messages to Twitter every
minutes from everywhere on the earth. By monitoring the Twitter stream, we canobtain various kinds of images with texts. In this paper, as a case study of Twitter
image mining for specific kinds of photos, we describe food photo mining fromthe Twitter stream. To collect food photos from Twitter, we monitor the Twitter stream to find the tweets containing both food-related keywords and photos,
and apply a “foodness” classifier and 100-class food classifiers to them to verify
whether they represent foods or not after downloading the corresponding photos.
In the paper, we report the experimental results of our food photo mining for theTwitter photo data we have collected for two years and four months. As results,
we detected about 470,000 food photos from Twitter. With this data, we madespatio-temporal analysis on food photos.
Keywords: photo tweet, Twitter mining, food photos, food classifier,
UEC-FOOD100.

1 Introduction
Twitter is a unique microblog, which is different from conventional social media interms of its quickness and on-the-spot-ness. Many Twitter’s users send messages, whichis commonly called “tweets”, to Twitter on the spot with mobile phones or smart
phones, and some of them send photos and geotags as well as tweets. Most of thephotos are sent to Twitter soon after taken. In case of food photos, most of them aretaken just before eating on the spot.
In this paper, we focus on food photos embedded in Tweets as a case study on a largescale Twitter photo analysis. Food is one of frequent topics in Tweets with photos. In
fact, we can see many food photos in lunch and dinner time in the Twitter stream.
Then, in this paper, by combining keyword-based search and food image recognition,
we mine food photos from the Twitter stream. To collect food photos from Twitter, wemonitor the Twitter stream to find the tweets containing both food-related keywords
and photos, and apply a “foodness” classifier and 100-class food classifiers to them toverify whether they shows foods or not after downloading the corresponding photos.
We used the state-of-the-art Fisher Vector coding with HoG and Color patches for foodclassifiers which is slightly modified with the rapid food recognition method for mobile
environments proposed in [6], and trained them with the UEC-FOOD100 dataset [9]11

http://foodcam.mobi/dataset/

W.T. Ooi et al. (Eds.): PCM 2014, LNCS 8879, pp. 22–32, 2014.
c Springer International Publishing Switzerland 2014


Twitter Food Photo Mining and Analysis

23

which consists of 100 kinds of foods commonly eaten in Japan. Since we employ the
improved method of the real-time mobile recognition, it takes only 0.024 seconds torecognize one image and it achieved about 83% classification rate within the top fivecandidates.
In the experiments, we report the results of our food photo mining on 100 kinds offoods in the UEC-FOOD100 dataset from the photo tweet log data we have collected
for two years and four months. As results, we detected about 470,000 food photos fromTwitter with about 99% accuracy. With this data, we have made spatio-temporal analysis
on food photos. In addition, we have implemented the real-time food photo detection
system from the Twitter stream.

2 Related Works
In this section, we mention about some representative works on Twitter photo mining.
As a representative work on Twitter mining, Sakaki et al. [12] regarded Twitter users
as social sensors which monitor and report the current status of the places where theusers are. They proposed a system which estimates the location of natural events suchas typhoons and earthquakes. They used geotagged tweets to estimate event locations
but no photos attached to tweets.
As early works on tweet photos, Yanai proposed World Seer [13] which can visualize geotagged photo tweets on the online map in the real-time way by monitoring
the Twitter stream. Nakaji et al. [10] proposed a system to mine representative photos
related to the given keyword or term from a large number of geo-tweet photos. They
extracted representative photos related to events such as “typhoon” and “New Year’sDay”, and successfully compared them in terms of the difference on places and time.
However, their system needs to be given event keywords or event term by hand. Kaneko
et al. [5] extended it by adding event keyword detection to the visual Tweet mining system. As results, they detected many photos related to seasonal events such as festivals
and Christmas as well as natural phenomena such as snow and Typhoon including extraordinary beautiful sunset photos taken around Seattle. All of these works focused on
only geotagged tweet photos.
On the other hand, Chen et al. [2] treated with photo tweets regardless of geoinformation. They analyzed relation between tweet images and messages, and defined
the photo tweet which has strong relation between its text message and its photo content as a “visual” tweet. In the paper, they proposed the method which is based on the
LDA topic model to classify “visual” and “non-visual” tweets. However, because their
method was generic and assumed no specific targets, the classification rate was only
70.5% in spite of two-class classification. Because we focus on only food photos unlike
their work, we use specialized object classifiers and have achieved very high classification accuracy. By using the method for real-time mobile food recognition, we can apply
it to more than one million and seven hundred thousand tweet images and implement areal-time food Tweet photo detection system.

24

K. Yanai and Y. Kawano

3 Overview
In this section, we describe an overview of the proposed method to mine food photos
from the stored Twitter logs as well as the Twitter stream. We employ the following
three-step processing.
(1) We perform keyword-based search with the names of target foods over a set ofphoto Tweets.
(2) We apply a newly-proposed “foodness” classifier to the tweet photos selectedby the keyword-based search for classifying then into either of “food” or “nonfood” photo.
(3) We apply individual food classifiers corresponding to the food names. In theexperiments, we prepared multi-class discriminative classifiers trained by SVM
with the UEC-FOOD100 dataset in the one-vs-rest manner.
The food classifiers employed in the third step is a slight modification of the method
for mobile food recognition proposed in [6], while the foodness classifier is newly proposed for removing non-food photos.

4 Detail of the Proposed Method
4.1 Keyword-Based Photo Tweet Selection
In the first step, we select photo tweets by keyword-based search with the names of thetarget foods. We search tweet message texts for the words of the target food names.
As the target foods, we used 100 kinds of foods in the UEC-FOOD100 dataset in
the experiments. Because the UEC-FOOD100 dataset includes common foods in Japansuch as ramen noodle, curry, and sushi, we searched only photo tweets the message
texts of which are written in Japanese language. We can easily select them by checking
the language attribute of each tweet obtained from the Twitter Streaming API.
4.2 Foodness Classifier
We construct a “Foodness” Classifier (FC) for discriminating food images from nonfood images. FC evaluates if the given image is a food photo or not. We use FC toremove noise images from the images gathered from the tweet photos selected by thefood names.
We construct a FC from the existing multi-class food image dataset. Firstly, we train
linear SVMs [4] in the one-vs-rest strategy for each category of the existing multi-classfood image dataset. As image features, we adopt HOG patches [3] and color patches
in the same way as [6]. Although HOG patches are similar local features to SIFT [8],HoG can be extracted much faster than SIFT. Both descriptors are coded by Improved
Fisher Vector (IFV) [11], and they are integrated in the late fusion manner. We perform
multi-class image classification in the cross-validation using the trained liner SVMs,
and we build a confusion matrix according to the classification results. In the experiments, we used 64 GMMs for IFV coding and two-level spatial pyramid [7], which ismuch improved from mobile food recognition [6] in terms of the feature dimension.

Twitter Food Photo Mining and Analysis

25

Table 1. 13 food groups and their member foods for the “foodness” classifier
type of food groupfood categories
noodlesudon nooles, dipping noodles, ramen
yellow coloromlet, potage, steamed egg hotchpotch
soupmiso soup, pork miso soup, Japaneses tofu and vegetable chowder
friedtakoyaki, Japaneses-style pancake, fried noodle
deep friedcroquette, sirloin cutlet, fried chicken
salad
green salad, macaroni salad, macaroni salad
bread
sandwiches, raisin bread, roll breadseafoodsashimi, sashimi bowl, sushi
rice
rice, pilaf, fried ricefish
grilled salmon, grilled pacific saury, dried fish
boiledseasoned beef with potatoes
simmered ganmodoki
andseasonedseasoned beef with potatoes
sauteedsauteed vegetables, go-ya chanpuru, kinpira-style sauteed burdock
sauce
stew, curry, stir-fried shrimp in chili sauce

Secondly, we make some category groups based on confusion matrix of multi-classclassification results. This is inspired by Bergamo et al.’s work [1]. They grouped alarge number of categories into superordinate groups the member categories of which
are confusing to each other recursively. In the same way, we perform confusion-matrixbased clustering for all the food categories. We intend to obtain superordinate categories
such as meat, sandwiches, noodle and salad automatically. As results, in the experiments, we obtained 13 food groups as shown in Table 1.
To build a “foodness” classifier (FC), we train a linear SVM of each of the superordinate categories. The objective of FC is discriminating a food photo from a non-foodphoto, which is different from the objective of the third step for discriminating a specificfood photo from other kinds of food photos. Therefore, abstracted superordinate categories are desirable to be trained, rather than training of all the food categories directly.
The output value of FC is the maximum value of SVM output of all the superordinate
food groups.
When training SVMs, we used all the images of the categories under the superordinate category as positive samples. For negative samples, we built a negative food image
set in advance by gathering images using the Web image search engines with query keywords which are expected to related to noise images such as “street stall”, “kitchen”,“dinner party” and “restaurant” and excluding food photos by hand. All the images are
represented by Fisher Vector of HoG patches and color patches. SVMs are trained in
the late fusion manner with uniform weights.
In the second step, we apply FC for the selected tweet photos and remove non-foodphotos from the food photo candidates.
4.3 Specific Food Classifiers
In this step, we classify a given photo into one of the prepared food classes.

26

K. Yanai and Y. Kawano

First, we extract HOG patches and Color patches in a dense grid sampling manner inthe same way as the previous step. Then, we apply PCA to all the extracted local features, and encode them into Improved Fisher Vectors. The method to extract features is
the same as the previous step including the parameter settings. Next, we evaluate linear
classifiers in the one-vs-rest way by calculating dot-product FVs. Finally we output the
top-N categories in terms of the descending order of evaluation scores of all the linear
classifiers.
In the experiments, we regarded the given tweet photo as a photo of the corresponding food if the food names contained in the tweet messages are ranked in the top fivecategories by evaluation of 100-kind food classifiers. This is because the top-5 classification rate exceeds 83%, while the top-1 rate is still around 60%.

5 Experimental Results
In this section, we describe the detail of the 100-class food dataset, the results of foodphoto mining from the Twitter stream, and some analysis on Twitter food data.
5.1 100 Food Categories and Their Classifiers
In the experiments, as target foods, we used 100 foods in the UEC-FOOD100 [9]2 , because we employ supervised food photo classification which requires training data to
select the target foods in the third step. It contains more than 100 images per category,
and all the food item in which are marked with bounding boxes. For training and evaluation, we used only the regions inside the given bounding boxes. The total number offood images in the dataset is 12,905. Figure 1 shows all the category names and theirsample photos. As shown in the figure, the dataset consists of common foods in Japan.
Then, we restricted tweets from which we mine food photo tweets to only the tweets
with Japanese messages, as mentioned in Section 4.1.
In [6], they implemented a mobile food recognition system using the same dataset.
Although basically we followed their method for individual food classification in thethird step, we extended the parameter setting to improved accuracy. To say it concretely,
we doubled the size of GMM for FV encoding from 32 to 64, and added two-level spatial pyramid. As a result, the total feature dimension are raised from 3072 to 35840,
which boosted the classification performance evaluated by 5-fold cross-validation asshown in Figure 2. Regarding the processing time, it takes only 0.024 seconds per
image to recognize on Core i7-3770K 3.50GHz with multi-threaded implementation
optimized for a quad-core CPU.
5.2 Twitter Food Mining
In this subsection, we describe the experimental results on twitter food photo mining.
We have been collecting photo tweet logs by monitoring the Twitter stream by using2

http://foodcam.mobi/dataset/Twitter Food Photo Mining and Analysis

chicken-’n’-egg on
pork cutlet on rice
rice

rice

eels on ricepilaf

beef currysushi

chicken rice

fried rice

tempura bowl

bibimbap

toastcroissantroll breadraisin breadchip butty

hamburger

pizza

sandwiches

udon noodle

tempura udon

soba noodle

ramen noodle

beef noodle

tensin noodle

fried noodle

spaghetti

Japanese-stylepancaketakoyakigratinsauteedvegetables

croquette

grilled eggplant sauteed spinach

vegetable
tempuramiso soup

potagesausageodenomeletganmodoki

jiaozistew

teriyaki grilledfish

fried fish

grilled salmon

salmon meunieresashimigrilled pacificsaurysukiyakisweet and sour
porklightly roasted
fish

steamed egg
hotchpotch

tempurafried chicken

sirloin cutlet

nanbanzuke

boiled fishseasoned beef
with potatoes

hambarg steak

steak

dried fish

ginger pork saute

spicychili-flavored tofu

yakitoricabbage rollomeletegg sunny-side up

natto

cold tofu

beef and simmered pork
chilled noodle stir-fried
peppersboiled chicken
and vegetables

sashimi bowl

sushi bowl

fish-shaped
pancake withbean jamshrimp with chillsourceroast chicken

steamed meat
dumpling

omelet with friedricecutlet curry

spaghetti meatsauce

fried shrimp

potato salad

green saladmacaroni salad

Japanese tofu andpork miso soup
vegetable
chowderchinese soup

beef bowl

rice ball

pizza toast

dipping noodles

french friesgoya chanpuru

kinpira-stylesauteed burdock

egg roll

hot dog27

mixed riceFig. 1. 100 kinds of foods prepared in the UEC-FOOD100 dataset. See this figure with magnification in the PDF viewer.

Twitter Streaming API. Here, we used 122,328,337 photo tweets with Japanese messages out of 988,884,946 photo tweets over all the world collected from May 2011 toAugust 2013 for two years and four months.
From these photo tweets, we selected 1,730,441 photo tweets the messages of which
include any of the name words of the 100 target foods in the first step of the proposed
processing flow. Then, in the second step, we applied a “foodness” classifier (FC) to
all the selected images. After applying FC, we applied 100-class one-vs-rest individual
food classifiers. As a result, we obtained 470,335 photos which are judged as food photos corresponding to any of the 100 target food categories by our proposed processing
pipeline.
For the 470,335 selected photos as food photos, we evaluate the number of selected
photos for each category. Table 2 shows the ranking of 100 food categories in terms ofthe number of mined tweet food photos. The number of “Ramen noodle” and “curry”
photos are the most and the second most with the large margin to the third or less ranked
food categories, respectively. In fact, “ramen” and “curry” are regarded as the mostpopular foods in Japan. “Sushi”, “dipping noodle (called as Tsukemen in Japanese)”

28

K. Yanai and Y. Kawano

Fig. 2. Comparison on the top-k classification rates with the UEC-FOOD100 dataset evaluatedby 5-fold cross validation between [6] and this paper.

Fig. 3. Examples of “omelet” photos. Most of them have drawings drawn by ketchup.

and “omelet with fried rice (called as Ome-rice in Japanese)” are also popular foods
in Japan. The results of twitter food image mining reflects food preference of Japanesepeople. In addition, we found that many of “ome-rice” had drawings or letters drawn
with ketchup, as shown in Figure 3. These are estimated to be made at home, whilemost of “ramen” and “sushi” photos are taken at food restaurants, because there aremany ramen noodle and sushi restaurant in Japan. Although “hamburger” and “beefbowl” are also popular in Japan as fast food served at fast-food restaurants such as
McDonald and Yoshino-ya, they are ranked at more than twentieth. This is because thefoods provided by nation-wide fast-food chain restaurants such as McDonaldo are thesame everywhere in the same chain restaurants, and they are not worth posting theirphotos to Twitter. On the other hand, since there are no monopolistic restaurant chainson ramen noodle and curry in Japan, the foods served at every ramen or curry restaurants
have originality and are different from each other.
Next, we evaluated the precision rate of the selected food photos in the each steps
regarding the top five foods and two sub-categories of “ramen noodle” and “curry”.
Table 3 shows the results in case of four types of the combinations of the three kindsof the selection methods, (1) only keywords, (1)+(2) keywords and foodness classifier(FC), (1)+(3) keywords and individual food classifier(IFC), and (1)+(2)+(3) keywords,
FC and IFC. Note that this evaluation was done for the 300 random-sampled photos foreach cell in the table.

Twitter Food Photo Mining and Analysis

29

Table 2. The ranking of 100 foods in terms of the number of mined tweet food photos
1ramen noodle
80021
2
curry
59264
3
sushi
25898
4
dipping noodle
22158
5 omelet with fried rice 17520
6
pizza
16921
7
jiaozi
16014
8 Japanese-style pancake 15234
9
steamed rice14264
10
sashimi13927
11
hambarg steak
11583
12
beef stake9503
13
takoyaki
9004
14
fried rice8383
15
fried noodle
7905
16
oden
7453
17
toast
6350
18
cutlet curry6339
19
tempura5905
20
rice ball5462
21
gratin5223
22croquette
4837
23
stew
4797
24
sashimi bowl
4730
25 chicken-’n’-egg on rice 4513
26
tempura bowl
4464
27
beef bowl
4285
28 spicy chili-flavored tofu 4081
29
yakitori
3829
30
hamburger
3662
31chilled noodle
3473
32
sukiyaki
3408
33
miso soup
3295

34 fish-shaped pancake with bean jam 3281
35
pork cutlet on rice3188
36 omelet with grilled minced meat 2592
37
bibimbap
2368
38
spaghetti
2171
39
lightly roasted fish2162
40 seasoned beef with potatoes 2129
41
natto
2094
42 spaghetti with meat source 1994
43 steamed egg hotchpotch 184344
egg sunny-side up
1635
45
croissant1579
46udon noodle
1500
47
simmered pork
1443
48
mixed sushi
1371
49
pork miso soup1229
50
ginger-fried pork
1158
51
potato salad
1150
52
egg omelet
1146
53
eels on rice1071
54
egg roll
1058
55
sweet and sour pork
1049
56
fried shrimp
1049
57
sauteed vegetables
1040
58 shrimp with chill source 1003
59
cabbage roll
965
60
mixed rice
901
61
pilaf
891
62
soba noodle
880
63
potage816
64
hot dog795
65
chicken rice736
66
wiener sausage
577

67
dried fish563
68
steamed meat dumpling
561
69
french fries
561
70
beef ramen noodle
555
71
sandwiches
551
72
cold tofu
517
73 boiled chicken and vegetables 352
74
sirloin cutlet
331
75
nanbanzuke
323
76
fried chicken314
77 stir-fried beef and peppers 312
78
roll bread
288
79
roast chicken
263
80
macaroni salad
239
81
boiled fish228
82 kinpira-style sauteed burdock 225
83
tempura udon
213
84
raisins bread
205
85
goya chanpuru
198
86
green salad
145
87chinese soup141
88 Japanese tofu and vegetable chowder 137
89
salmon meuniere
96
90
grilled pacific saury
84
91
chip butty
76
92
fried fish
72
93
begitable tempura
71
94
tensin noodle
69
95
ganmodoki
34
96
grilled salmon
25
97
sauteed spinach
12
98
teriyaki grilled fish
3
99
grilled eggplant
2
100
pizza toast
0

Regarding (1), the precision of two sub-categories, “beef ramen noodle” and “cutlet curry”, are relatively higher, 94.3% and 92.7%, than “ramen noodle” and “curry”.
From this results, we can assume that when tweeting detailed food names with photos, the photos probably represent the corresponding foods. After applying both FC and
IFC, (1)+(2)+(3), the precision of all the seven foods achieved the best compared tothe cases of applying only single methods or only keyword-based search, (1), (1)+(2)and (1)+(3). Except for “sushi”, the precision reached 99.0%, which means nearly perfect. This shows the effectiveness of introducing both FC and IFC after keyword-based
search. Exceptionally, “sushi” is a difficult food to recognize by object recognition
methods, because the appearances of “sushi” varies greatly depending on the kindsof the ingredients on the pieces of hand-rolled rice.
Finally, we describe simple spatio-temporal analysis on Twitter food photos. Figure
4 shows the prevailing-food map where the red marks, the yellow marks and the blue
marks represent the areas where “ramen noodle”, “curry” and “okonomiyaki” are mostpopular in terms of the number of food photo tweets, respectively. The left map, the center map, and the right map show the prevailing-food map on all the term (May 2011Aug. 2013), Dec. 2012 (in winter), and Aug. 2013 (in summer), respectively. Fromthe leftmost map, “ramen noodle” is the most popular over Japan on average througha year. However, compared between the center map and the rightmost map, popularity of “curry” increases in summer, while “ramen noodle” becomes the most popular

30

K. Yanai and Y. Kawano

Table 3. The number of selected photos and their precision(%) with four different combinations
food category(1)(1)+(2)(1)+(3)(1)+(2)+(3)ramen noodle
275652 (72.0%) 200173 (92.7%) 84189 (95.0%) 80021 (99.7%)
beef ramen noodle
861 (94.3%)
811 (99.0%)558 (99.7%)555 (99.7%)
curry224685 (75.0%) 163047 (95.0%) 62824 (97.0%) 59264 (99.3%)
cutlet curry
10443 (92.7%)9073 (98.0%) 6544 (98.7%) 6339 (99.3%)
sushi
86509 (69.0%) 43536 (86.0%) 48019 (72.3%) 25898 (92.7%)
dipping noodle
33165 (88.7%) 24896 (96.3%) 28846 (93.7%) 22158 (99.0%)
omelet with fried rice 34125 (90.0%) 28887 (96.3%) 18370 (98.0%) 17520 (99.0%)

Fig. 4. The prevailing-food map of Japan. See the text.

in winter. Exceptionally, in the area around Hiroshima where the blue marks appear,“okonomiyaki” is always the prevailing food in Twitter food photos, this is partly because Hiroshima has a very popular regional food, “Hiroshima-yaki”, which is a variantof “okonomiyaki”.
As another temporal analysis on the mined food photos, we examined the time wheneach food are eaten the most frequently over a day. As results, the most frequent time
when “ramen noodle” and “curry” are eaten is between 12pm and 2pm, while the mostfrequent time of “sushi” and “okonomiyaki” is between 7pm and 9pm. This reflects thedifference of the characteristic of the foods. As shown in this subsection, the data wecollected through Twitter food photo mining is useful for food habit analysis.
5.3 Real-Time Food Photo Detection System
We implemented a real-time Twitter food photo detection system which continuously
detects 100 kinds of food photos from the Twitter stream. We detect the photo tweets
including any of 100 kinds of Japanese food names about ten times per minute at most.
Because the time to download a thumbnail image is about 2 or 3 seconds and the processing time for food recognition for each image is less than 0.1 seconds, we can process
all the pipeline on a single machine in the real-time way. The very fast food recognition
method which was originally designed for a mobile application made it possible.

Twitter Food Photo Mining and Analysis

31

As shown in Figure 5, the detected food photos are shown on the map if they havegeotags or geo-related words such as place names in their Tweet messages, and on theright side the photos are displayed as the results by online k-means clustering. Thissystem can be accessible via http://mm.cs.uec.ac.jp/tw/.

Fig. 5. Detected food photos are displayed on the map when geo-information are available

6 Conclusions
In this paper, we described food photo mining from the Twitter stream as a case studyof specific categories of tweet photo mining. To do that, we proposed the three-step processing consisting of keyword-based selection with food category names, classificationof food or non-food photos, and visual verification of the correspondence between theextracted food words and the food category of the tweet photo. In addition, we showed
the collected data was useful for various kinds of analysis of foods.
Currently we always keep running the real-time food photo detection system and collecting new food photos. For example, we are collecting about 20,000 “ramen noodle”
and 15,000 “curry” photos per month. When the number of both food images exceeds
one million, we will release them as a large-scale food dataset for research purpose,
which we expect enables fine-grained food recognition research.

References
1. Bergamo, A., Torresani, L.: Meta-class features for large-scale object categorization on abudget. In: Proc. of IEEE Computer Vision and Pattern Recognition (2012)
2. Chen, T., Lu, D., Kan, M.Y., Cui, P.: Understanding and classifying image tweets. In: Proc.
of ACM International Conference Multimedia, pp. 781–784 (2013)

32

K. Yanai and Y. Kawano

3. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proc. of IEEE
Computer Vision and Pattern Recognition (2005)
4. Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: LIBLINEAR: A library for large
linear classification. The Journal of Machine Learning Research 9, 1871–1874 (2008)
5. Kaneko, T., Yanai, K.: Visual event mining from geo-tweet photos. In: Proc. of IEEE ICMEWorkshop on Social Multimedia Research (SMMR) (2013)
6. Kawano, Y., Yanai, K.: Rapid mobile object recognition using fisher vector. In: Proc. of Asian
Conference on Pattern Recognition (2013)
7. Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matchingfor recognizing natural scene categories. In: Proc. of IEEE Computer Vision and Pattern
Recognition (2006)
8. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journalof Computer Vision 60(2), 91–110 (2004)
9. Matsuda, Y., Yanai, K.: Multiple-food recognition considering co-occurrence employing
manifold ranking. In: Proc. of IAPR International Conference on Pattern Recognition (2012)
10. Nakaji, Y., Yanai, K.: Visualization of real world events with geotagged tweet photos. In:
Proc. of IEEE ICME Workshop on Social Media Computing (SMC) (2012)
11. Perronnin, F., Sánchez, J., Mensink, T.: Improving the fisher kernel for large-scale image
classification. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS,
vol. 6314, pp. 143–156. Springer, Heidelberg (2010)12. Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes twitter users: real-time event detection by social sensors. In: Proc. of the International World Wide Web Conference,
pp. 851–860 (2010)
13. Yanai, K.: World seer: a realtime geo-tweet photo mapping system. In: Proc. of ACM International Conference on Multimedia Retrieval (2012)