Abstract
Understanding where humans look in a scene is significant for many applications. Researches on neuroscience and cognitive psychology show that human brain always pays attention on special areas when they observe an image. In this paper, we recorded and analyzed human eye-tracking data, we found that these areas mainly were focus on semantic objects. Inspired by neuroscience, deep learning concept is proposed. Fully Convolutional Neural Networks (FCN) as one of methods of deep learning can solve image objects segmentation at semantic level efficiently. So we bring forth a new visual attention model which uses FCN to stimulate the cognitive processing of human free observing a natural scene and fuses attractive low-level features to predict fixation locations. Experimental results demonstrated our model has apparently advantages in biology.
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Acknowledgements
We would like to thank the associate editor and all of the reviewers for their constructive comments to improve the manuscript. The work is supported by NSF of China (Nos. NCYM0001 and 61201319).
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Li, N., Zhao, X., Ma, B., Zou, X. (2018). A Visual Attention Model Based on Human Visual Cognition. In: Ren, J., et al. Advances in Brain Inspired Cognitive Systems. BICS 2018. Lecture Notes in Computer Science(), vol 10989. Springer, Cham. https://doi.org/10.1007/978-3-030-00563-4_26
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