Abstract
In this paper, we propose a clustering method in order to effectively present image search results on the Web. In order to reflect the difference of image semantics among the images, we use the meta-tags added by social tagging. Furthermore, we use low-level image features and photo metadata in order to consider the image looks and photo-taking conditions. We applied constrained agglomerative clustering method with must-link constraints for better clustering results by using multiple similarity metric. We conducted experiments to demonstrate that the proposed method effectively clusters image search results comparing to the traditional weighted similarity aggregation approach, and in some cases the clustering performance of our approach is better than other existing approaches for clustering tasks on an online photo sharing site.
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References
Flickr, http://www.flickr.com/
Bhattacharyya, A.: On a measure of divergence between two statistical populations defined by their probability distribution. In: Bull. Calcutta Math. Soc. (1943)
Boutell, M.R., Luo, J.: Bayesian fusion of camera metadata cues in semantic scene classification. In: CVPR, vol. (2), pp. 623–630 (2004)
Chatzichristofis, S.A., Boutalis, Y.S.: Cedd: Color and edge directivity descriptor: A compact descriptor for image indexing and retrieval. In: Gasteratos, A., Vincze, M., Tsotsos, J.K. (eds.) ICVS 2008. LNCS, vol. 5008, pp. 312–322. Springer, Heidelberg (2008)
Davidson, I., Ravi, S.S.: Agglomerative hierarchical clustering with constraints: Theoretical and empirical results. In: Jorge, A.M., Torgo, L., Brazdil, P.B., Camacho, R., Gama, J. (eds.) PKDD 2005. LNCS (LNAI), vol. 3721, pp. 59–70. Springer, Heidelberg (2005)
Fowlkes, E.B., Mallows, C.L.: A method for comparing two hierarchical clusterings. Journal of the American Statistical Association, 553–569 (1983)
Leelanupab, T., Halvey, M., Jose, J.M.: Application and evaluation of multi-dimensional diversity. In: ImageClef 2009 Workshop, Corfu, Greece (September 2009)
Meilă, M.: Comparing clusterings: an information based distance. Journal of Multivariate Analysis (2007)
Moëllic, P.-A., Haugeard, J.-E., Pitel, G.: Image clustering based on a shared nearest neighbors approach for tagged collections. In: CIVR, pp. 269–278 (2008)
Salembier, P., Sikora, T.: Introduction to mpeg-7: Multimedia content description interface. Hohn Wiley & Sons, Inc., New York (2002)
Smeulders, A.W.M., Worring, M., Santini, S., Gupta, A., Jain, R.: Content-based image retrieval at the end of the early years. IEEE Transactions on pattern analysis and machine intelligence 22(12), 1349–1380 (2000)
Tamura, H., Mori, S., Yamawaki, T.: Textural features corresponding to visual perception. IEEE Transactions on Systems, Man and Cybernetics 8(6), 460–473 (1978)
van Leuken, R.H., Pueyo, L.G., Olivares, X., van Zwol, R.: Visual diversification of image search results. In: WWW, pp. 341–350 (2009)
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Hirota, M., Yokoyama, S., Fukuta, N., Ishikawa, H. (2010). Constraint-Based Clustering of Image Search Results Using Photo Metadata and Low-Level Image Features. In: Lee, R. (eds) Computer and Information Science 2010. Studies in Computational Intelligence, vol 317. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15405-8_14
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DOI: https://doi.org/10.1007/978-3-642-15405-8_14
Publisher Name: Springer, Berlin, Heidelberg
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