An Improved Saliency Detection Algorithm Based on Edge Boxes and Bayesian Model

Abstract

Visual saliency detection aims to extract salient objects from the original image, making it less complicated to process the image. This paper combines an edge box algorithm with Bayesian theory to detect salient objects. The proposed saliency detection algorithm transforms the process of traditional detection method, and prioritizes the positioning of significant objects. Firstly, the Harris corners of the original image were calculated, and clustered by the improved clustering algorithm, yielding the number of salient objects in the image. Then, all possible positions of salient objects in the image were framed by the edge box algorithm, and the boxes were sorted in descending order of the score. According to the number N of clusters of the image corners, the N top-ranking boxes were selected to determine the salient regions. In this way, the position and number of salient objects were clarified. Based on the selected salient regions, the final saliency map was calculated by improved geodesic distance and Bayesian model. Experimental results show that our approach performed better than 11 existing algorithms in both simple and relatively complex scenes. In terms of objective performance, the accuracy and recall of our algorithm on MSRA10k, ECSSD, DUT-OMRON and SED2 datasets were higher than that of the other algorithms.