A bio-inspired center-surround model for salience computation in images

• A bio-inspired center-surround model is proposed for salience computation.
• The center-surround model draws inspiration from retinal photoreceptor interactions.
• Multiple nonlinear combinations of nearby pixel values implement the interactions.
• We show that the combinations constitutes a favorable information-theoretic content.
• Qualitative and quantitative results show the effectiveness of the proposed approach.

A center-surround model inspired by photoreceptor interactions and visual receptive field organization is presented in this paper for salience computation that predicts human eye fixation locations in images. The essence of photoreceptor interactions is implemented considering different nonlinear combinations of responses to stimuli given by values at nearby image pixels. These combinations are then fed to difference of Gaussian filtered outputs operation and Gabor filter based processes simulating visual receptive field organization. The proposed center-surround model is used in Itti et al.’s bio-inspired framework to perform salience computation. Analysis is carried out to present the information-theoretic aspect of the nonlinear combinations. Significance of the proposed center-surround model is shown both qualitatively and quantitatively by comparing its use in salience computation with the use of existing models considering different psychological patterns, and synthetic and real-life images. Quantitative and qualitative performance of salience computation using the novel center-surround model for three well-known datasets of images are also compared to that of relevant existing salience computation approaches to demonstrate the effectiveness of the proposed approach in generating salience maps closer to human eye fixation density maps.