Salient Object Detection based on Multiple Priors Fusion Salient object detection can be utilized to detect the most significant regions in various environments, which has been regarded as foundation of computer vision. Different saliency models use different prior or knowledge. We propose a multi priors fusion method for saliency measure, which integrates background prior with foreground prior and center prior. Firstly, through each boundary of the image, we can get four saliency maps, and fuse them to get the background prior saliency map. Secondly, we utilize boundary extension method to highlight regions, and these regions can be regarded as the queries of manifold ranking for the foreground prior saliency map. Thirdly, the corners on the image are obtained, filtered by the foreground region, and then clustered into a point as the center of Gaussian model, which is used to calculate the center prior saliency map. Finally, the above three kinds of priorbased saliency maps are fused via the proposed fusion framework to gain a better final saliency map. Compared with fifteen methods, the experimental results on ECSSD and MSRA10K show that our proposed method achieves better saliency detection results.

Salient Object Detection based on CNN Fusion of Two Types of Saliency Models Salient object detection has attracted significant attention, and recently many heuristic computational models have been developed for object detection. In this paper, we introduced a new approach that is based on convolutional neural network fusion strategy to combine the saliency maps generated by highdimensional color transform and salient object detection integrating discriminative regional features methods. Our method is based on the observation of salient regions and has a distinctive role in removing background information, which in humanxe2x80x99s perspective is not required in the object detection task. The key contribution of this work is a pipeline of CNN based fusion method, which not only performs best but also generates the saliency maps closer to the ground truth. The experimental results show that our method demonstrates the effectiveness of the proposed pipeline while comparing it with other stateoftheart methods.