Supervised training and contextually guided salient object detection

Existing salient object detection research generally focused on designing diverse saliency features and integrating them heuristically. In this paper, a novel salient object detection method is proposed by employing supervised training and contextual modeling. Gradient boosting decision trees are explored to aggregate features on segmented regions using a supervised training manner. Feature representation of hierarchically segmented regions is exploited to capture salient objects at different levels so as to extract discriminative features better. A region-level pairwise conditional random field (CRF) method is constructed to further boost the accuracy of saliency estimation as well as to improve the perceptual consistency of saliency maps. Experimental results demonstrate that the proposed method could achieve state-of-the-art performance over all public datasets. The F-measure is improved by 3.9%, 13.0%, 4.3% on the MSRA-B, DUT-OMRON and HKU-IS dataset respectively, and the mean absolute error (MAE) is reduced by 31.6%, 26.4% and 21.2% respectively on these three datasets.