Saliency Optimization of Background Detection Based on Synchronous Updating

Abstract

The existing saliency detection methods have many limitations,such as large detection error,strong subjectivity and too little restriction on the background prior knowledge.So this paper proposes a synchronization update optimization method based on the saliency of backgrounddetection.Through changing the constraints of background prior,the similarity degrees of significant graphs and truth graphs are calculated,and the confidence metric is used to synchronize the propagation update.This method strengthens the correlation between adjcent pixels and makes significant target edge clearer.Experimental results on the standard dataset show that,the optimization algorithm significantly improves the detection accuracy compared with existing background-based saliency detection methods.