  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
those 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.
