TY - GEN
T1 - Predicting emotional states of images using Bayesian multiple kernel learning
AU - Zhang, He
AU - Gönen, Mehmet
AU - Yang, Zhirong
AU - Oja, Erkki
PY - 2013
Y1 - 2013
N2 - Images usually convey information that can influence people's emotional states. Such affective information can be used by search engines and social networks for better understanding the user's preferences. We propose here a novel Bayesian multiple kernel learning method for predicting the emotions evoked by images. The proposed method can make use of different image features simultaneously to obtain a better prediction performance, with the advantage of automatically selecting important features. Specifically, our method has been implemented within a multilabel setup in order to capture the correlations between emotions. Due to its probabilistic nature, our method is also able to produce probabilistic outputs for measuring a distribution of emotional intensities. The experimental results on the International Affective Picture System (IAPS) dataset show that the proposed approach achieves a bette classification performance and provides a more interpretable feature selection capability than the state-of-the-art methods.
AB - Images usually convey information that can influence people's emotional states. Such affective information can be used by search engines and social networks for better understanding the user's preferences. We propose here a novel Bayesian multiple kernel learning method for predicting the emotions evoked by images. The proposed method can make use of different image features simultaneously to obtain a better prediction performance, with the advantage of automatically selecting important features. Specifically, our method has been implemented within a multilabel setup in order to capture the correlations between emotions. Due to its probabilistic nature, our method is also able to produce probabilistic outputs for measuring a distribution of emotional intensities. The experimental results on the International Affective Picture System (IAPS) dataset show that the proposed approach achieves a bette classification performance and provides a more interpretable feature selection capability than the state-of-the-art methods.
KW - Image emotion
KW - Low-level image features
KW - Multiple kernel learning
KW - Multiview learning
KW - Variational approximation
UR - http://www.scopus.com/inward/record.url?scp=84893414123&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84893414123&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-42051-1_35
DO - 10.1007/978-3-642-42051-1_35
M3 - Conference contribution
AN - SCOPUS:84893414123
SN - 9783642420504
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 274
EP - 282
BT - Neural Information Processing - 20th International Conference, ICONIP 2013, Proceedings
T2 - 20th International Conference on Neural Information Processing, ICONIP 2013
Y2 - 3 November 2013 through 7 November 2013
ER -