Flickr tag recommendation based on collective knowledge

Online photo services such as Flickr and Zooomr allow users to share their photos with family, friends, and the online community at large. An important facet of these services is that users manually annotate their photos using so called tags, which describe the contents of the photo or provide additional contextual and semantical information. In this paper we investigate how we can assist users in the tagging phase. The contribution of our research is twofold. We analyse a representative snapshot of Flickr and present the results by means of a tag characterisation focussing on how users tags photos and what information is contained in the tagging. Based on this analysis, we present and evaluate tag recommendation strategies to support the user in the photo annotation task by recommending a set of tags that can be added to the photo. The results of the empirical evaluation show that we can effectively recommend relevant tags for a variety of photos with different levels of exhaustiveness of original tagging.

[1]  Christiane Fellbaum,et al.  Book Reviews: WordNet: An Electronic Lexical Database , 1999, CL.

[2]  Marcel Worring,et al.  Content-Based Image Retrieval at the End of the Early Years , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Lada A. Adamic.  Zipf, Power-laws, and Pareto-a ranking tutorial , 2000 .

[4]  James Ze Wang,et al.  Real-Time Computerized Annotation of Pictures , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Peter Mika,et al.  Ontologies are us: A unified model of social networks and semantics , 2005, J. Web Semant..

[6]  Mor Naaman,et al.  World explorer: visualizing aggregate data from unstructured text in geo-referenced collections , 2007, JCDL '07.

[7]  Ravi Kumar,et al.  Visualizing tags over time , 2006, WWW '06.

[8]  W. Reed.  The Pareto, Zipf and other power laws , 2001 .

[9]  Laura A. Dabbish,et al.  Labeling images with a computer game , 2004, AAAI Spring Symposium: Knowledge Collection from Volunteer Contributors.

[10]  Mor Naaman,et al.  Why we tag: motivations for annotation in mobile and online media , 2007, CHI.

[11]  Mor Naaman,et al.  Towards automatic extraction of event and place semantics from flickr tags , 2007, SIGIR.

[12]  James Ze Wang,et al.  Image retrieval: Ideas, influences, and trends of the new age , 2008, CSUR.

[13]  Bernardo A. Huberman,et al.  Usage patterns of collaborative tagging systems , 2006, J. Inf. Sci..

[14]  Mor Naaman,et al.  HT06, tagging paper, taxonomy, Flickr, academic article, to read , 2006, HYPERTEXT '06.

[15]  David A. Forsyth,et al.  Matching Words and Pictures , 2003, J. Mach. Learn. Res..

[16]  W. Bruce Croft,et al.  Deriving concept hierarchies from text , 1999, SIGIR '99.

[17]  Kristina Lerman,et al.  Social Browsing on Flickr , 2006, ICWSM.

[18]  Mor Naaman,et al.  ZoneTag: Rich, community-supported context-aware media capture and annotation , 2007 .

[19]  P. Schmitz,et al.  Inducing Ontology from Flickr Tags , 2006 .

[20]  Bernardo A. Huberman,et al.  The Structure of Collaborative Tagging Systems , 2005, ArXiv.