Efficient Saliency Aggregation via Soft-Voting Evolution
Ling Zheng, Shuhan Chen, Xuelong Hu, Lifeng Zhang · 2016
Efficient saliency detection algorithm plays an important role in computer vision tasks, such as object recognition, visual tracking, image compression, image segmentation and so on. A variety of saliency detection methods have been proposed in recently, which often complement each other. In this paper we propose a soft-voting evolution saliency aggregation algorithm which combines them. First of all, we set a threshold TH which is used to segment the background and foreground region. Then, each map can be seen as a voter, and it is voted as foreground when the saliency value is greater than TH. In contrast, it is voted as background when the saliency is less than TH. Different with previous works, we define a soft weight based on sigmoid function. Besides, iterative operation is necessary to further improve performance. Experiments on three publicly available datasets demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods.