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Published in:
2016 | OriginalPaper | Chapter
Biologically Plausible Saliency Detection Model
Authors : Natalia Efremova, Sergey Tarasenko
Published in: Biologically Inspired Cognitive Architectures (BICA) for Young Scientists
Publisher: Springer International Publishing
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Abstract
We propose a novel neural network architecture for visual saliency detections, which utilizes neurophysiologically plausible mechanisms for extraction of salient regions. The model has been significantly inspired by recent findings from neurophysiology and aimed to simulate the bottom-up processes of human selective attention. Two types of features were analyzed: color and direction of maximum variance. The mechanism we employ for processing these features is PCA, implemented by means of normalized Hebbian learning and the waves of spikes. To evaluate performance of our model we have conducted psychological experiment. Comparison of simulation results with those of experiment indicates good performance of our model.