Inaccurate Supervised Saliency Detection Based on Iterative Feedback Framework

Machine learning based bottom-up saliency detection (MLBU) methods are very popular recently. These MLBU methods firstly use prior knowledge to select some regions from the given image as training samples and label them. Based on training set, a saliency classifier is learned to classify salient object and background by applying machine learning algorithms in the given image. Nevertheless, training labels obtained by prior knowledge are not always accurate in some complex scenes, inaccurate training set is hard to make subsequent learning process succeed. To solve this problem, we propose an inaccurate supervised learning (ISL) based saliency detection framework, which assumes that training labels obtained by prior knowledge might be inaccurate and constructs three checking rules to remove mislabeled samples for more accurate training set construction. The refined training set is used to learn a saliency classifier which can better predict each image region. To obtain more accurate saliency inference, the proposed ISL process is introduced into a novel iterative feedback (IF) framework to generate better saliency result. Finally, we use smoothness operator to further smooth saliency result for performance improvement. Experimental results on three benchmark datasets demonstrate adequately the superiority of the proposed method.