WWW 2009 MADRID!

Poster Sessions: Friday, April 24, 2009

Mining Cultural Differencesfrom a Large Number of Geotagged Photos
Keiji Yanai

Bingyu Qiu

The University of Electro-Communications
Chofu, Tokyo 182-8585 JAPANBeijing University of Posts and Technology
Beijing, 100876, China

yanai@cs.uec.ac.jp

bigyuqiu@bupt.cn

ABSTRACT
We propose a novel method to detect cultural diﬀerences
over the world automatically by using a large amount of geotagged images on the photo sharing Web sites such as Flickr.
We employ the state-of-the-art object recognition techniquedeveloped in the research community of computer vision tomine representative photos of the given concept for representative local regions from a large-scale unorganized collectionof consumer-generated geotagged photos. The results help
us understand how objects, scenes or events corresponding
to the same given concept are visually diﬀerent dependingon local regions over the world.
Categories and Subject Descriptors: H.3.3 Information
Search and Retrieval: Miscellaneous
General Terms: Algorithms, Experimentation, Measurement
Keywords: geotag, object recognition, representative image, Flickr

for helping us understand how local speciﬁc objects or scenes
over the world are diﬀerent.
Our objective is thus to facilitate a system which can automatically select relevant and representative photographs
for the general object or scene concepts in the worldwide
dimensions. In particular, we consider geotagged photos on
Flickr, identify the representative image groups, and generate an aggregate representation based on locations thatallows navigation, exploration and understanding of the differences of general concepts depending on local locationsin the world visually. We employ the state-of-the-art object recognition technique developed in the research community of computer vision to mine representative photos of
the given concept for each region from a large-scale unorganized collection of consumer-generated geotagged photos.
The results help us understand how objects, scenes or events
corresponding to the same given concept are visually diﬀerent depending on local regions over the world.

1. INTRODUCTION
Recently, consumer-generated media (CGM) on the Web
has become very popular. Especially, photo sharing sites
such as Flickr and Picasa are representative CGM sites,
which store a huge number of consumer-generated photos
people uploaded, and make them accessible via the Web foreveryone. Photo sharing sites collect not only photos but
also metadata on uploaded photos. As metadata users add
to photos, textual information such as keywords and comments is common. Recently, in addition to texts, some users
attach “geotags” to their uploaded photos. Note that a “geotag” means metadata which represents a location where thecorresponding photo was taken, which is expressed by a setof a latitude and a longitude.
An accurate geotag can be obtained with a GPS device
or a location-aware camera-phone. However, since it forces
us to use relatively special devices, GPS-based geotags havenot been common so far. Instead, map-based geotags havebecome common, after Flickr, which is the largest photosharing site in the world, launched an online geotagging interface in 2006. Then, Flickr also became the largest “geotagged” photo database in the world. According to [3], there
are currently over 40,000,000 public geotagged photos on
Flickr, and 100,000 geotagged photos have been added every month. These geotagged photos would be valuable not
only for browsing and ﬁnding individual concepts, but also
Copyright is held by the author/owner(s).
WWW2009, April 20-24, 2009, Madrid, Spain.
ACM 978-1-60558-487-4/09/04.

Figure 1: After collecting geotagged photo related
to the given concept by the text-tag-based search,
we remove noise images, cluster regions and selectregional representative images.

2.

OVERVIEW OF OUR APPROACH

Our approach for selecting the representative images forrepresentative local regions from geotagged images consistsof three main stages (Figure 1): (1) removing irrelevant images to the given concept, (2) estimating representative geographic regions, and (3) selecting representative images foreach region.
First, we apply clustering techniques to partition the image set into similar groups, based on bag-of-visual-words
feature vectors [1]. By evaluating the intra-cluster densities
as well as the cluster member numbers, we discard most of
the irrelevant images and obtain a reduced set of images
which are visually similar to each other. This stage could
be regarded as the “Filtering Stage”.
Then, we geographically cluster the reduced set of images and select large geographic clusters as representativeregions. Here we use the k-means clustering algorithm based1173

WWW 2009 MADRID!
on the geographic latitude and longitude of photos to obtainrepresentative regions in the world for the given concept.
Finally, for each representative region, we perform theProbabilistic Latent Semantic Analysis (PLSA) [2] to identify the distinct “topics”, do additional clustering on the entire topic vectors, and select the “signiﬁcant” cluster as therepresentative results for this geographic region. In addition,with the help of on-line map service, we have implemented
a map-based browser to show selected representative photos for understanding of diﬀerences regarding appearancesof generic object concepts over the world.
Please refer to [4] for the detail.

Poster Sessions: Friday, April 24, 2009

Figure 2: ”Noodle” in Japan. Chinese-style noodle“ramen” is popular.

3. EXPERIMENTAL RESULTS
To test and verify if our approach works in practice, weconducted preliminary experiments with photos collected directly from Flickr. In the experiments, we used seven “object” concepts and two “scene” concepts including “noodle”,“wedding cake”, “ﬂower”, ”castle”, “car”, “waterfall” and“beach”. For each concept, we collected about 2000 mostrelevant geotagged photos distributed evenly in the world
wide areas. The precision of raw photos of these seven concepts is 43% on average, which is deﬁned as (# relevantimages)/(# all images). After “Filtering Stage”, it was improved to 80%, which indicated that noise removal was effective.
We show the representative photos selected for several representative regions, while these regions were generated automatically based on geographic locations of the most relevant
photos selected in the “Filtering Stage”. Figure 2 and Figure
3 show the results for the concept “noodle”, each of which
presents the most representative photos generated for theapproximate regions: Japan and Europe. Without doubt,
these results can help us understand about the “noodle”in these local areas. For example, Figure 2 demonstrates
many “ramen” photos in Japan and Figure 3 demonstrates“spaghetti” photos in the European area. In addition, South
East Asia, Mideast US and Western US are obtained asother representative regions, representative photos of which
also have characteristics such as “noodles” in the South East
Asia area containing some Taiwanese style noodles and spicy
Thai noodles.
Figure 4 and Figure 5 correspond to “wedding cake” inEurope and in Mid US, respectively. We can ﬁnd many of
the wedding cakes in Mid US are much taller than ones inEurope.
For the scene concept “waterfall”, we extracted the representative photos for four large regions: Asia, Europe, North
America, and South America. From the results, we can ﬁndthat waterfalls in South America seem to be more powerful,while waterfalls in the Asian area are somehow more beautiful. Such kinds of implications would be helpful in guiding
travels around the world.
To see more results, please visit the following website:
http://img.cs.uec.ac.jp/yanai/ASRP/ .

4. CONCLUSIONS AND FUTURE WORKIn this paper, we present a novel topic which is for thepurpose of generating representative photographs for typical regions in the world for mining cultural diﬀerences on
the given concepts, and provide an approach to achieve itwith the help of geotagged collections. The results help us
understand how objects or scenes associated with the same

Figure 3: ”Noodle” in Europe. Most of photos are“Spaghetti”.

Figure 4: ”Wedding cake” in Mid US. Tall cakes arecommon. This is ﬁve-layered.

Figure 5: ”Wedding cake” in Europe.
much shorter and simpler than US.

They areconcept are diﬀerent depending on local regions in the world
visually.
For future work, we plan to make extensive experiments
for more concepts from a larger set of photos, and thinkout some other strategies in detecting more representative
regions with a more precise scope. In addition, we will conduct some quantitative evaluations on the representativeness of the photos selected for the corresponding region andthe diﬀerences of the tendency of representative photo sets
among diﬀerent local regions.

5.

REFERENCES[1] G. Csurka, C. Bray, C. Dance, and L. Fan. Visual
categorization with bags of keypoints. In Proc. of ECCV
Workshop on Statistical Learning in Computer Vision,
pages 59–74, 2004.
[2] T. Hofmann. Unsupervised learning by probabilistic latentsemantic analysis. Machine Learning, 43:177–196, 2001.
[3] L. Kennedy and M. Naaman. Generating diverse andrepresentative image search results for landmarks. In Proc.
of the International World Wide Web Conference, pages
297–306, 2008.
[4] B. Qiu and K. Yanai. Objects over the world. In Proc. ofPaciﬁc-Rim Conference on Multimedia, pages 296–305,
2008.

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