Contrast-Based Visual Saliency Estimation
Saliency estimation has become a valuable tool in image processing. Yet, existing approaches exhibit considerable variation in methodology, and it is often difficult to attribute improvements in result quality to specific algorithm properties. In this work, we reconsider some of the design choices of previous methods and propose a conceptually clear and intuitive algorithm for contrast-based saliency estimation.
Our algorithm consists of four basic steps. First, our method decomposes a given image into compact, perceptually homogeneous elements that abstract unnecessary detail. Based on this abstraction, we compute two measures of contrast that rate the uniqueness and the spatial distribution of these elements. From the element contrast we then derive a saliency measure that produces a pixel-accurate saliency map that uniformly covers the objects of interest and consistently separates foreground and background. We show that the complete contrast and saliency estimation can be formulated in a uniﬁed way using high dimensional Gaussian ﬁlters. This result contributes to the conceptual simplicity of our method and lends itself to a highly efﬁcient implementation with linear complexity. In a detailed experimental evaluation, we analyze the contribution of each individual feature and show that our method outperforms all state-of-the-art approaches at the time of publication