%0 Conference Paper %1 Abbasi2009LST %A Abbasi, Rabeeh %A Grzegorzek, Marcin %A Staab, Steffen %B Semantic Multimedia %D 2009 %I Springer Berlin / Heidelberg %K 2009 folksonomies geo lowlevel recommendation samt tag tags %P 65--76 %R 10.1007/978-3-642-10543-2_8 %T Large Scale Tag Recommendation Using Different Image Representations %U http://www.springerlink.com/content/k2476uxvq123j101/ %V 5887 %X Nowadays, geographical coordinates (geo-tags), social annotations (tags), and low-level features are available in large image datasets. In our paper, we exploit these three kinds of image descriptions to suggest possible annotations for new images uploaded to a social tagging system. In order to compare the benefits each of these description types brings to a tag recommender system on its own, we investigate them independently of each other. First, the existing data collection is clustered separately for the geographical coordinates, tags, and low-level features. Additionally, random clustering is performed in order to provide a baseline for experimental results. Once a new image has been uploaded to the system, it is assigned to one of the clusters using either its geographical or low-level representation. Finally, the most representative tags for the resulting cluster are suggested to the user for annotation of the new image. Large-scale experiments performed for more than 400,000 images compare the different image representation techniques in terms of precision and recall in tag recommendation. %@ 978-3-642-10542-5

@inproceedings{Abbasi2009LST, abstract = {Nowadays, geographical coordinates (geo-tags), social annotations (tags), and low-level features are available in large image datasets. In our paper, we exploit these three kinds of image descriptions to suggest possible annotations for new images uploaded to a social tagging system. In order to compare the benefits each of these description types brings to a tag recommender system on its own, we investigate them independently of each other. First, the existing data collection is clustered separately for the geographical coordinates, tags, and low-level features. Additionally, random clustering is performed in order to provide a baseline for experimental results. Once a new image has been uploaded to the system, it is assigned to one of the clusters using either its geographical or low-level representation. Finally, the most representative tags for the resulting cluster are suggested to the user for annotation of the new image. Large-scale experiments performed for more than 400,000 images compare the different image representation techniques in terms of precision and recall in tag recommendation.}, added-at = {2009-12-09T10:33:04.000+0100}, author = {Abbasi, Rabeeh and Grzegorzek, Marcin and Staab, Steffen}, biburl = {https://www.bibsonomy.org/bibtex/27de70a9af28874ff06d407c0f1d2697c/rabeeh}, booktitle = {Semantic Multimedia}, doi = {10.1007/978-3-642-10543-2_8}, interhash = {e22e59a9d76b06554394ad23fdf3d67e}, intrahash = {7de70a9af28874ff06d407c0f1d2697c}, isbn = {978-3-642-10542-5}, issn = {0302-9743 (Print) 1611-3349 (Online)}, keywords = {2009 folksonomies geo lowlevel recommendation samt tag tags}, pages = {65--76}, publisher = {Springer Berlin / Heidelberg}, series = {Lecture Notes in Computer Science}, subject_collection = {Computer Science}, timestamp = {2009-12-09T10:35:33.000+0100}, title = {Large Scale Tag Recommendation Using Different Image Representations}, url = {http://www.springerlink.com/content/k2476uxvq123j101/}, volume = 5887, year = 2009 }