Affiliations: [a] Department of Computer Architecture, Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran | [b] Department of Artificial Intelligent, Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran
Correspondence:
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Corresponding author: Ahmad Reza Naghsh-Nilchi, Department of Artificial Intelligent, Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran. E-mail:[email protected]
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.
Keywords: Affective music video retrieval, Dempster-Shafer theory, information fusion, information retrieval, emotion detection
