Bio-driven visual saliency detection with color factor.

Abstract

Most visual saliency computing methods build models based on the content of an image without considering the colorized effects. Biologically, human attention can be significantly influenced by color. This study firstly investigates the sole contribution of colors in visual saliency and then proposes a bio-driven saliency detection method with a color factor. To study the color saliency despite the contents, an eye-tracking dataset containing color images and gray-scale images of the same content is proposed, collected from 18 subjects. The CIELab color space was selected to conduct extensive analysis to identify the contribution of colors in guiding visual attention. Based on the observations that some particular colors and combinations of color blocks can attract much attention than others, the influence of colors on visual saliency is represented computationally. Incorporating the color factor, a novel saliency detection model is proposed to model the human color perception prioritization, and a deep neural network model is proposed for eye fixation prediction. Experiments validate that the proposed bio-driven saliency detection models make substantial improvements in finding informative content, and they benefit the detection of salient objects which are close to human visual attention in natural scenes.