Abstract
Saliency map analysis provides an alternative methodology to image semantic understanding in many applications such as adaptive content delivery and region-based image retrieval. A lot of visual saliency map algorithms have been proposed during the last decades. Recent psychophysical research further reveals that visual attention can be modulated and improved by the affective significance of stimuli, called Emotional Attention, which might not only supplement but also compete with other sources of top-down control on attention. Inspired by this mechanism, we propose a novel computational emotional attention model in this paper. In particular, we present an intuitive emotional saliency map computation method by calculating Minkowski-norm of pixel’s isolated saliency value and multi-scale local contrast information in color emotion space. Experimental results on diverse image datasets show that the proposed model can outperform some current state-of-the-art visual saliency map.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Fecteaua, J.H., Munoz, D.P.: Salience, relevance, and firing: a priority map for target selection. Trends Cogn. Sci. 10(8), 382–390 (2006)
Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE TPAMI 20(11), 1254–1259 (1998)
Hou, X., Zhang, L.: Saliency detection: a spectral residual approach. In: CVPR, pp. 1–8 (2007)
Valenti, R., Sebe, N., Gevers, T.: Image saliency by isocentric curvedness and color. In: ICCV, pp. 2185–2192 (2009)
Harel, J., Koch, C., Perona, P.: Graph-based visual saliency. In: NIPS, pp. 545–552 (2006)
Achanta, R., Hemami, S., Estrada, F., Susstrunk, S.: Frequency-tuned salient region detection. In: CVPR, pp. 1597–1604 (2009)
Eastwood, J.D., Smilek, D., Merikle, P.M.: Differential attentional guidance by unattended faces expressing positive and negative emotion. Percept. Psychophysics 63(6), 1004–1013 (2001)
Vuilleumier, P.: How brains beware: neural mechanisms of emotional attention. TRENDS Cogn. Sci. 9(12), 585–594 (2005)
Anderson, A.K.: Affective influences on the attentional dynamics supporting awareness. J. Exp. Psychol. 134(2), 258–281 (2005)
Carretie, L., Hinojosa, J.A., Martin-Loeches, M.: Automatic attention to emotional stimuli: neural correlates. Hum. Brain Mapp. 22(4), 290–299 (2004)
Ou, L., Luo, M., Woodcock, A., Wright, A.: A study of colour emotion and colour preference. Part I. Col. Res. App. 29(3), 232–240 (2004)
Andersen, S.K.: Color-selective attention need not be mediated by spatial attention. J. Vis. 9(6), 1–7 (2009)
Liu, T., Sun, J., Zheng, N.N., Tang, X.: Learning to detect a salient object. In: CVPR, pp. 1–8 (2007)
Li, B., Xiong, W., Wu, O., et al.: Horror image recognition based on context-aware multi-instance learning. IEEE Trans. Image Process. 24(12), 5193–5205 (2015)
Peng, H., Li, B., Ji, R., Hu, W.: Salient object detection via low-rank and structured sparse matrix decomposition. In: The 27th AAAI Conference on Artificial Intelligence (2013)
Li, B., Xiong, W., Hu, W.: Visual saliency map from tensor analysis. In: The 26th AAAI Conference on Artificial Intelligence (2012)
Acknowlegment
This work is supported by the National Nature Science Foundation of China (No. 61303086, 61370038, 61571045, 61472227, 61503219, 61572296), National Nature Science Foundation of Shandong province (No. ZR2013FM015, ZR2015FL020) and Domestic Visiting Schoars Project of Shandong Provincial Education Department.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Ding, X., Huang, L., Li, B., Lang, C., Hua, Z., Wang, Y. (2016). A Novel Emotional Saliency Map to Model Emotional Attention Mechanism. In: Tian, Q., Sebe, N., Qi, GJ., Huet, B., Hong, R., Liu, X. (eds) MultiMedia Modeling. MMM 2016. Lecture Notes in Computer Science(), vol 9517. Springer, Cham. https://doi.org/10.1007/978-3-319-27674-8_18
Download citation
DOI: https://doi.org/10.1007/978-3-319-27674-8_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-27673-1
Online ISBN: 978-3-319-27674-8
eBook Packages: Computer ScienceComputer Science (R0)