Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Albert, J.H., Chib, S.: Bayesian analysis of binary and polychotomous response data. Journal of the American Statistical Association 88(422), 669–679 (1993)
Beal, M.J.: Variational Algorithms for Approximate Bayesian Inference. Ph.D. thesis, The Gatsby Computational Neuroscience Unit, University College London (2003)
Cortes, C., Vapnik, V.: Support-vector networks. Machine Learning 20(3), 273–297 (1995)
Gönen, M., Alpaydın, E.: Multiple kernel learning algorithms. Journal of Machine Learning Research 12, 2211–2268 (2011)
Hanjalic, A.: Extracting moods from pictures and sounds: Towards truly personalized TV. IEEE Signal Processing Magazine 23(2), 90–100 (2006)
Laaksonen, J., Koskela, M., Oja, E.: PicSOM-self-organizing image retrieval with MPEG-7 content descriptors. IEEE Transactions on Neural Networks 13(4), 841–853 (2002)
Lawrence, N.D., Jordan, M.I.: Semi-supervised learning via Gaussian processes. In: Advances in Neural Information Processing Systems 17, pp. 753–760 (2005)
Lu, X., Suryanarayan, P., Adams Jr., R.B., Li, J., Newman, M.G., Wang, J.Z.: On shape and the computability of emotions. In: Proceedings of the International Conference on Multimedia, pp. 229–238 (2012)
Machajdik, J., Hanbury, A.: Affective image classification using features inspired by psychology and art theory. In: Proceedings of the International Conference on Multimedia, pp. 83–92 (2010)
Mikels, J., Fredrickson, B., Larkin, G., Lindberg, C., Maglio, S., Reuter-Lorenz, P.: Emotional category data on images from the International Affective Picture System. Behavior Research Methods 37(4), 626–630 (2005)
Picard, R.: Affective Computing. MIT Press (1997)
Sjöberg, M., Muurinen, H., Laaksonen, J., Koskela, M.: PicSOM experiments in TRECVID 2006. In: Proceedings of the TRECVID 2006 Workshop (2006)
Zhang, H., Augilius, E., Honkela, T., Laaksonen, J., Gamper, H., Alene, H.: Analyzing emotional semantics of abstract art using low-level image features. In: Gama, J., Bradley, E., Hollmén, J. (eds.) IDA 2011. LNCS, vol. 7014, pp. 413–423. Springer, Heidelberg (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhang, H., Gönen, M., Yang, Z., Oja, E. (2013). Predicting Emotional States of Images Using Bayesian Multiple Kernel Learning. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing. ICONIP 2013. Lecture Notes in Computer Science, vol 8228. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42051-1_35
Download citation
DOI: https://doi.org/10.1007/978-3-642-42051-1_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-42050-4
Online ISBN: 978-3-642-42051-1
eBook Packages: Computer ScienceComputer Science (R0)