This study aims to evaluate a new fusion technique of visual and textual clusters of objects from a real multimedia data-driven collection to improve the performance of multimedia applications.
The authors focused on using multi-criteria for clustering texts and images. The algorithm consists of these steps: first is text representation using the statistical method of weighting, second is image representation using a bag of words feature descriptors methods and finally application of multi-criteria clustering.
As an application for event detection based on social multimedia data, in particular, Flickr platform. Several experiments were conducted to choose the appropriate parameters for a better scheme of clustering. The new approach achieves better performance when aggregate text clustering is done with image clustering for event detection.
Further researches would be investigated on other social media platforms such as Facebook and Twitter for a generalization of the technique.
This study contributes to multimedia data mining through the new fusion technique of clustering. The technique has its root in such strong field as the field of multi-criteria clustering and decision-making support.
Khatir, N. and Nait-bahloul, S. (2018), "Multi-criteria-based fusion for clustering texts and images case study on Flickr", Kybernetes, Vol. 47 No. 10, pp. 1973-1991. https://doi.org/10.1108/K-01-2018-0030Download as .RIS
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