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Robust saliency detection via corner information and an energy function

In this study, the authors propose a distinctive bottom-up visual saliency detection algorithm based on a new background prior and a new reinforcement. Inspired by genetic algorithm, the final map is obtained with three steps. First of all, the authors construct a background-based saliency map by manifold ranking via superior image corners selected by convex-hull as background prior, which is different from most of the existing background prior-based methods treated all image boundaries as background. Then, a better result is obtained by ranking the relevance of the image elements with foreground seeds extracted from the preliminary saliency map. Furthermore, a novel optimisation framework is introduced with the intention of refining the map, which integrates an energy function with a guided filter. Experimental results on three public datasets indicate that the proposed method performs favourably against the state-of-the-art algorithms.