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The currently best performing state-of-the-art saliency detection algorithms incorporate heuristic functions to evaluate saliency. They require parameter tuning, and the relationship between the parameter value and visual saliency is often not well understood. Instead of using parametric methods we follow a ma- chine learning approach, which is parameter free, to estimate saliency. Our method learns data-driven saliency-estimation functions and exploits the contributions of visual properties on saliency. First, we over-segment the image into superpixels and iteratively connect them to form hierarchical image segments. Second, from these segments, we extract biologically- plausible visual features. Finally, we use regression trees to learn the relationship between the feature values and visual saliency. We show that our algorithm outperforms the most recent state-of-the-art methods on three public databases.
Pascal Frossard, Chenglin Li, Li Wei, Qin Yang, Yuelei Li, Hao Wang
Michael Herzog, Ben Henrik Lönnqvist, Adrien Christophe Doerig, Alban Bornet
Silvestro Micera, Daniela De Luca