An evidential data fusion method for affective music video retrieval

Abstract

Affective video retrieval systems seek to retrieve video contents concerning their impact on viewers' emotions. These systems typically apply a multimodal approach that fuses information from different modalities to specify the affect category. The main drawback of existing information fusion methods exploited in affective video retrieval systems is that they consider all modalities equally important; hence they ignore conflicts among modalities. In order to address this drawback, a new information fusion method is proposed based on the Dempster-Shafer theory of evidence. This proposed method assigns different weights to modalities based on their correlation and their level of confidence. Experiments are run on the video clips of DEAP dataset. Results indicate that the proposed method outperforms existing evidential information fusion methods significantly. © 2017 - IOS Press and the authors. All rights reserved.