Over-sampling strategy-based class-imbalanced salient object detection and its application in underwater scene

One major branch of bottom-up salient object detection methods is machine learning-based methods which learn to classify salient object(positive) and background(negative) based on various learning algorithms. Generally, each input image corresponds to a training set which is determined by prior knowledge. However, we find that the class-imbalance problem (i.e., positive and negative data are seriously imbalanced in quantity) is inevitable for these methods due to the existence of various factors. Imbalanced training set might fail to make learning process succeed. To solve above problem, we propose a novel bottom-up Salient object detection algorithm based on Class-Imbalance Learning(CILS). For the input image, our goal is to ensure that a robust saliency classifier is well learned even if class-imbalance problem occurs in training set. To this end, we propose a novel over-sampling strategy which concentrates on constructing synthetic samples to make two classes be balanced. As a result, the balanced training data could be better applied to subsequent learning and classification process. Finally, the saliency map obtained by CILS is further refined by a novel optimization framework via foreground consistency and background consistency. Our proposed algorithm and other state-of-the-art algorithms are tested on three datasets. Results demonstrate adequately that our model produces better performance than other compared algorithms. Furthermore, we apply our salient object detection algorithm to underwater object recognition task and recognition accuracy could be further improved by introducing saliency cues.