Abstract : Recognition of emotional semantics in images is a new and very challenging research direction that gains more and more attention in the research community. As an emerging topic, publications remains relatively rare and numerous issues need to be addressed. In this paper, we propose to investigate the efficiency of different types of features including low-level features and proposed semantic features for classification of emotional semantics in images. Moreover, we propose a new approach that combines different classifiers based on Dempster-Shafer’s theory of evidence, which has the ability to handle ambiguous and uncertain knowledge such as the properties of emotions. Experiments driven on the International Affective Picture System (IAPS) image databases, which is a common stimulus set frequently used in emotion psychology research, demonstrated that the proposed approach can achieve promising results.
Type de document :
Communication dans un congrès
International Conference on Computer Vision, Theory and Applications (VISAPP), Mar 2011, Vilamoura, Algarve, Portugal. pp.352-357, 2011
https://hal.archives-ouvertes.fr/hal-01354394
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Soumis le : jeudi 18 août 2016 - 19:25:36
Dernière modification le : vendredi 19 août 2016 - 01:04:21
Ningning Liu, Emmanuel Dellandréa, Bruno Tellez, Liming Chen. Evaluation of Features and Combination Approaches for the Classification of Emotional Semantics in Images. International Conference on Computer Vision, Theory and Applications (VISAPP), Mar 2011, Vilamoura, Algarve, Portugal. pp.352-357, 2011. <hal-01354394>
