Salient Object Detection based on Background Optimization

Abstract

Aiming at the problems of low detection accuracy and blurred object edges in current salient object detection based on background algorithms, a new algorithm based on boundary prior to estimate background is proposed. Firstly, the super-pixel image segmentation algorithm (SLIC) is used to segment the image into super-pixels. According to the theory that the foreground and background are relative, the background is estimated by using the four-edges boundary prior of the image; At the same time, an optimal fusion algorithm is proposed to fuse the prior background and foreground with optimal weights to obtain the final saliency map. Quantitative and qualitative comparisons with 10 classic salient object detection algorithms, the results show that the proposed algorithm can not only detect salient objects of different sizes more accurately, but also effectively suppress background redundant information, preserving the object boundary. Therefore, the accuracy of our algorithm is better than 10 classical algorithms. The algorithm in this paper which serve as pre-processing for computer vision has great promise in the field of intelligent control of machine vision.