Saliency Map Improvement Using Edge-Aware Filtering

Abstract

Content-aware applications in computational photography define the relative importance of objects or actions present in an image using a saliency map. Most saliency detection algorithms learn from the human visual system and try to find relatively important content as a salient region(s). This paper attempts to improve the saliency map defined by these algorithms using an iterative process. The saliency map of an image generated by an existing saliency detection algorithm is modified by filtering the image after segmenting into foreground and background. In order to enhance the saliency map values present in the salient region, the background is filtered using an edge-aware guided filter and the foreground is enhanced using a local Laplacian filter. The number of iterations required varies according to the image content. We show that the proposed framework enhances the saliency maps generated using the state-of-the-art saliency detection algorithms both qualitatively and quantitatively.