This paper describes the contributions of Vienna University of Technology (TUW) to the MediaEval 2015 Retrieving Diverse Social Images challenge. Our approach consists of 3 phases: (1) Precision-oriented-phase: in which we focus only on the relevance of the documents; (2) Recall-oriented-phase: in which we focus only on the diversity aspect; (3) Merging phase: in which we explore ways to find a balance between the relevance and diversity factors. We use two fusion methods for this last part. Our best run reached a F1@20 of 0.582. http://ceur-ws.org/Vol-1436/ http://www.multimediaeval.org

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TUW @ MediaEval 2015
Retrieving Diverse Social ImagesTask
SerwahSabetghadam,João Palotti, Navid Rekabsaz,
Mihai Lupu,Allan Hanbury
Vienna University ofTechnology

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Different Runs
Relevancy
Diversity
Fusion

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Relevancy Runs
Based on Word2Vec method (Mikrolov et al.)
T. Mikolov,K.Chen, G. Corrado, and J. Dean. Ecient estimationof word
representationsinvector space.arXiv preprintarXiv:1301.3781, 2013.

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Diversity Runs
Hand-codedrules (Jain et al.)
Removeif
• Distance> 8km
• Descriptionlength>
2000 characters
• Containedfrontal or
side-viewsof facesin focus
Word2Vec method • Ensemble ofClusters
• Spectral , Heirarchical,
Metis
• Features: CNN, CN3x3
N. Jain, J. Hare, S. Samangooei, J. Preston, J. Davies, D. Dupplaw, andP. H. Lewis. Experiments in
diversifying ickr result sets. In MediaEval2013, 2013.

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Fusion Runs
LinearWeighted
• Optimizationproblemto maximize the linear
combinationof relevancy & diversity results
Bayesian Inference
• Combinedbased on the rules of the
probabilitytheory

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Runs Configurations

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Runs Results
Best: Run3 with F1@20 of 0.58

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Conclusion & Future Work
 The combination of Relevancy and Diversity
method gave better result
n
3) provides robust results
 Adding diversity scores, verifying different
fusion methods
Questions?
See.you@the.Poster.session
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