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.
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References
Adrian, B., Sauermann, L., Roth-Berghofer, T.: Contag: A semantic tag recommendation system. In: Pellegrini, T., Schaffert, S. (eds.) Proceedings of I-Semantics 2007, September 2007, pp. 297–304. JUCS (2007)
Basile, P., Gendarmi, D., Lanubile, F., Semeraro, G.: Recommending smart tags in a social bookmarking system. In: Bridging the Gap between Semantic Web and Web 2.0 (SemNet 2007), pp. 22–29 (2007)
Bolettieri, P., Esuli, A., Falchi, F., Lucchese, C., Perego, R., Piccioli, T., Rabitti, F.: CoPhIR: a test collection for content-based image retrieval. CoRR, abs/0905.4627v2 (2009)
Cristani, M., Perina, A., Castellani, U., Murino, V.: Content visualization and management of geo-located image databases. In: CHI 2008: CHI 2008 extended abstracts on Human factors in computing systems, pp. 2823–2828. ACM, New York (2008)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2006)
Heymann, P., Ramage, D., Garcia-Molina, H.: Social tag prediction. In: SIGIR 2008: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, pp. 531–538. ACM, New York (2008)
Jäschke, R., Marinho, L., Hotho, A., Schmidt-Thieme, L., Stumme, G.: Tag recommendations in folksonomies. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) PKDD 2007. LNCS (LNAI), vol. 4702, pp. 506–514. Springer, Heidelberg (2007)
Kennedy, L., Naaman, M., Ahern, S., Nair, R., Rattenbury, T.: How flickr helps us make sense of the world: context and content in community-contributed media collections. In: MULTIMEDIA 2007: Proceedings of the 15th international conference on Multimedia, pp. 631–640. ACM, New York (2007)
Kennedy, L.S., Naaman, M.: Generating diverse and representative image search results for landmarks. In: WWW 2008: Proceeding of the 17th international conference on World Wide Web, pp. 297–306. ACM, New York (2008)
Mardia, K., Kent, J., Bibby, J.: Multivariate Analysis. Academic Press, London (1979)
Moëllic, P.-A., Haugeard, J.-E., Pitel, G.: Image clustering based on a shared nearest neighbors approach for tagged collections. In: CIVR 2008: Proceedings of the 2008 international conference on Content-based image and video retrieval, pp. 269–278. ACM, New York (2008)
Müller, H., Michoux, N., Bandon, D., Geissbuhler, A.: A review of content-based image retrieval systems in medical applications - clinical benefits and future directions. International Journal of Medical Informatics 73(1), 1–23 (2003)
Pentland, A., Picard, R., Sclaroff, S.: Tools for content-based manipulation of image databases. International Journal of Computer Vision 18(3), 233–254 (1996)
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Abbasi, R., Grzegorzek, M., Staab, S. (2009). Large Scale Tag Recommendation Using Different Image Representations. In: Chua, TS., Kompatsiaris, Y., Mérialdo, B., Haas, W., Thallinger, G., Bailer, W. (eds) Semantic Multimedia. SAMT 2009. Lecture Notes in Computer Science, vol 5887. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10543-2_8
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DOI: https://doi.org/10.1007/978-3-642-10543-2_8
Publisher Name: Springer, Berlin, Heidelberg
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