Real world datasets often consist of data expressed through multiple modalities. Clustering such datasets is in most cases a challenging task as the involved modalities are often heterogeneous. In this paper we propose a graph-based multimodal clustering approach. The proposed approach utilizes an example relevant clustering in order to learn a model of the “same cluster” relationship between a pair of items. This model is subsequently used in order to organize the items of the collection to be clustered in a graph, where the nodes represent the items and a link between a pair of nodes exists if the model predicted that the corresponding pair of items belong to the same cluster. Eventually, a graph clustering algorithm is applied on the graph in order to produce the final clustering. The proposed approach is applied on two problems that are typically treated using clustering techniques; in particular, it is applied on the problem of detecting social events and to the problem of discovering different landmark views in collections of social multimedia.
CITATION STYLE
Petkos, G., Schinas, M., Papadopoulos, S., & Kompatsiaris, Y. (2017). Graph-based multimodal clustering for social multimedia. Multimedia Tools and Applications, 76(6), 7897–7919. https://doi.org/10.1007/s11042-016-3378-2
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