This paper presents a bioinspired visual saliency model. The end-stopping mechanism in the primary visual cortex is introduced in to extract features that represent contour information of latent salient objects such as corners, line intersections and line endpoints, which are combined together with brightness, color and orientation features to form the final saliency map. This model is an analog for the processing mechanism of visual signals along from retina, lateral geniculate nucleus(LGN)to primary visual cortex V1:Firstly, according to the characteristics of the retina and LGN, an input image is decomposed into brightness and opposite color channels; Then, the simple cell is simulated with 2D Gabor filters, and the amplitude of Gabor response is utilized to represent the response of complex cell; Finally, the response of an end-stopped cell is obtained by multiplying the response of two complex cells with different orientation, and outputs of V1 and LGN constitute a bottom-up saliency map. Experimental results on public datasets show that our model can accurately predict human fixations, and the performance achieves the state of the art of bottom-up saliency model.
Learning-Based Visual Saliency Model for Detecting Diabetic Macular Edema in Retinal Image
