Detecting Non-Gaussian Geographical Topics in Tagged Photo Collections

Institute for Web Science & Technologies University of Koblenz ▪ Landau, Germany

Detecting Non-Gaussian Geographical Topicsin Tagged Photo Collections

Christoph Carl Kling, Jérôme Kunegis, Sergej Sizov, Steffen Staab

Detecting Non-Gaussian Geographical Topics 2Christoph Carl Kling

Outline

1) Motivation2) Existing approaches3) Our approach4) Evaluation


Motivation


Topics in topic modelling:

Latent variables that explain the co-occurrence of wordsin documents.


Topics in topic modelling:

Latent variables that explain the co-occurrence of wordsin documents.

Geographical topics:

Latent variables that explain the co-occurrence of wordsboth in documents and in the geographical space.


fish, rice

seafood, fish seafood, shrimp lobster, wine

seafood, fish, salmon

fish, salmon, wine

seafood, shrimp

lobster, seafood, shrimp

coffee

coffee, wine

coffee

wine

wine

pizza, wine

pizza, wine

pasta, wine

pasta, shrimp

lobster, shrimp

seafood, shrimp

Tagged photographies with geo-coordinates


fish, rice



fish, salmon, wine

seafood, shrimp


coffee

coffee, wine

coffeeitalian, wine

wine

pizza, wine

italian, pizza, wine

pasta, wine

pasta, shrimp

seafoodfishlobstershrimpcrabwinesalmon

winepizzacoffeeitalianpasta

seafood, shrimp

lobster, shrimp


Existing Approaches


fish, rice

lobster, shrimp



seafood, shrimp

fish, salmon, wine

seafood, shrimp


coffee

coffee, wine

coffeeitalian, wine

wine

pizza, wine


pasta, wine

pasta, shrimp

shrimpfishriceseafoodlobster


fishseafoodsalmonshrimpwine

seafoodshrimplobster

lobsterseafoodfishsalmonwineGeoFolk, S. Sizov 2010


fish, rice

lobster, shrimp



seafood, shrimp

fish, salmon, wine

seafood, shrimp


coffee

coffee, wine

coffeeitalian, wine

wine

pizza, wine


pasta, wine

pasta, shrimp



LGTA, Z. Yin et al., 2011


fish, rice

lobster, shrimp



seafood, shrimp

fish, salmon, wine

seafood, shrimp


coffee

coffee, wine

coffeeitalian, wine

wine

pizza, wine


pasta, wine

pasta, shrimp



A. Ahmed, L. Hong and A. Smola, 2013


Our Approach


Cultural areas, country borders, geographical features and other geographical observations exhibit complex spatial distributions

wikipedia.org


fish, rice

lobster, shrimp



seafood, shrimp

fish, salmon, wine

seafood, shrimp


coffee

coffee, wine

coffeeitalian, wine

wine

pizza, wine


pasta, wine

pasta, shrimp

Clustering:E.g. mixture of Gaussian/Fisher distributions


fish, rice

lobster, shrimp



seafood, shrimp

fish, salmon, wine

seafood, shrimp


coffee

coffee, wine

coffeeitalian, wine

wine

pizza, wine


pasta, wine

pasta, shrimp




Adjacency:Delaunay triangulationK-NN…


fish, rice

lobster, shrimp



seafood, shrimp

fish, salmon, wine

seafood, shrimp


coffee

coffee, wine

coffeeitalian, wine

wine

pizza, wine


pasta, wine

pasta, shrimp




Cluster adjacency Dependencies of document-specific topic distributions

Exchange of topic information between clusters


γ

M N

L

H

G

G

α0

G

Al

j

0

θjn

w

η s

d

l

δl

L: #regionsM: #documents in clusterN: #words in documentG :⁰ Global topic distributionG : Cluster-topic distributionG : Document-topic distribution

s

d

MGTM


Evaluation


Datasets

Activities: 1.931 photosLandscape: 5.791 photosManhattan: 28.922 photosCar: 34.707 photosFood: 151.747 photos

LGTA, Z. Yin et al., 2011


Compared models:

- LGTA: Model with regions- Basic model: 3-level Hierarchical Dirichlet Process- MGTM: Basic model plus dynamically

smoothed adjacent regions


manhattan (100 regions) landscape (200 regions)

activities (300 regions) car (500 regions) food (1000 regions)

Word Perplexity


User Study

Food dataset (1000 regions)31 participantsTask: intrusion detectionMeasure: precision

4 topicsavg / median



LGTA 0.67 / 0.64 0.57 / 0.57 0.60 / 0.58

Basic model 0.45 / 0.57 0.63 / 0.61 0.64 / 0.58

MGTM 0.79 / 0.80 0.82 / 0.81 0.78 / 0.75


west.uni-koblenz.deResearch → systems → MGTM

west.uni-koblenz.de liveandgov.eu


Thank you!Questions?

Contact: [email protected]


Summary

• Geographical topics often exhibit a complex spatial distribution

• The detection of such complex topics can be supported

• The dynamic smoothing of adjacent regions leads to an evolutionary creation and spread of topics during inference


ReferencesReferences

Hierarchical Dirichlet processesby: Y. W. Teh, M. I. Jordan, M. J. Beal, and D. M. BleiIn: Journal of the American Statistical Association, Vol. 101 (2006) , p. 1566-1581.

GeoFolk: latent spatial semantics in web 2.0 social media.by: Sergej SizovIn: WSDM ACM (2010) , p. 281-290.

Geographical topic discovery and comparison.by: Zhijun Yin, Liangliang Cao, Jiawei Han, Chengxiang Zhai, and Thomas S. HuangIn: WWW ACM (2011) , p. 247-256.

A Nonparametric Bayesian Model of Multi-Level Category Learning.by: Kevin Robert Canini, and Thomas L. GriffithsIn: AAAI AAAI Press (2011) .

Detecting Non-Gaussian Geographical Topics in Tagged Photo Collections

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