Salient Object Detection based on Background Optimization

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