Visual saliency detection via image complexity feature
Min Liu, Ke Gu, Guangtao Zhai, Patrick Le Callet · 2016
In this paper we propose a novel bottom-up visual saliency detection model by analysis of image complexity. Compared with existing works, we emphasize the important impact of image complexity on saliency detection. Inspired by the free energy theory, a hybrid parametric and non-parametric model is used to estimate the complexity of a visual signal. Taking the image complexity as a new feature, this paper constructs a heuristic framework to systematically combine two different types of saliency detection models, separately using local and global features, in order to predict human fixation points more accurately. In contrast to classical and modern models, our algorithm has achieved noticeably superior results. And furthermore, it is worthy to stress that the proposed saliency detection method can also help to facilitate the performance of image quality metrics on popular image databases.