Journal
COGNITIVE NEURODYNAMICS
Volume 5, Issue 4, Pages 321-332Publisher
SPRINGER
DOI: 10.1007/s11571-011-9155-z
Keywords
Visual attention; Saliency map; Fast algorithm; Discrete cosine transform; Principal component analysis
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Funding
- National Natural Science Foundation of China [61071134, 60672116]
- National High Technology Research and Development Program of China (863 Program) [2009AA12Z115]
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In this paper we propose a computational model of bottom-up visual attention based on a pulsed principal component analysis (PCA) transform, which simply exploits the signs of the PCA coefficients to generate spatial and motional saliency. We further extend the pulsed PCA transform to a pulsed cosine transform that is not only data-independent but also very fast in computation. The proposed model has the following biological plausibilities. First, the PCA projection vectors in the model can be obtained by using the Hebbian rule in neural networks. Second, the outputs of the pulsed PCA transform, which are inherently binary, simulate the neuronal pulses in the human brain. Third, like many Fourier transform-based approaches, our model also accomplishes the cortical center-surround suppression in frequency domain. Experimental results on psychophysical patterns and natural images show that the proposed model is more effective in saliency detection and predict human eye fixations better than the state-of-the-art attention models.
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