Multi-criteria-based fusion for clustering texts and images case study on Flickr

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

Further researches would be investigated on other social media platforms such as Facebook and Twitter for a generalization of the technique.

Originality/value

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.

Citation

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-01-2018-0030