An HVS-Oriented Saliency Map Prediction Modeling
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Visual attention is one of the most significant characteristics for selecting and understanding the outside world. The nature complex scenes, including larger redundancy and human vision, can't be processing all information simultaneously because of the information bottleneck. The visual system mainly focuses on dominant parts of the scenes to reduce the input visual redundancy information. It's commonly known as visual attention prediction or visual saliency map. This paper proposes a new saliency prediction architecture inspired by human low-level visual cortex function. The model considered the opponent color channel, wavelet energy map, and contrast sensitivity function for extract image features and maximum approach to real visual neural network function in the brain. The proposed model is evaluated several datasets, including MIT1003, MIT300, TORONTO, and SID4VAM to explain its efficiency. The proposed model results are quantitatively and qualitatively compared to other state-of-the-art salience prediction models and their achieved out-performing of visual saliency prediction.
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