Abstract
A common approach to the problem of SED in collections of multimedia relies on the use of clustering methods. Due to the heterogeneity of features associated with multimedia items in such collections, such a clustering task is very challenging and special multimodal clustering approaches need to be deployed. In this paper, we present a scalable graph-based multimodal clustering approach for SED in large collections of multimedia. The proposed approach utilizes example relevant clusterings to learn a model of the “same event” relationship between two items in the multimodal domain and subsequently to organize the items in a graph. Two variants of the approach are presented: the first based on a batch and the second on an incremental community detection algorithm. Experimental results indicate that both variants provide excellent clustering performance.
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Petkos, G., Papadopoulos, S., Schinas, E., Kompatsiaris, Y. (2014). Graph-Based Multimodal Clustering for Social Event Detection in Large Collections of Images. In: Gurrin, C., Hopfgartner, F., Hurst, W., Johansen, H., Lee, H., O’Connor, N. (eds) MultiMedia Modeling. MMM 2014. Lecture Notes in Computer Science, vol 8325. Springer, Cham. https://doi.org/10.1007/978-3-319-04114-8_13
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DOI: https://doi.org/10.1007/978-3-319-04114-8_13
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