The visual salience extraction model only considers visual contrasting information and it does not conform to the biology process of human eyes.Therefroe,a hybrid model based on Improved Salient Region Extraction(ISRE) algorithm was proposed in this paper.This hybrid model consists of a salience filtering algorithm and an improved Pulse Coupled Neural Network(PCNN) algorithm.Firstly,the salience filtering algorithm was used to get Original Salience Map(OSM) and Intensity Feature Map(IFM) was used as the input neuron of PCNN.Then,the PCNN ignition pulse input was further improved as follows:the point multiplication algorithm was taken between the PCNN internal neuron and the binarization salience image of OSM to determine the final ignition pulse input and to make the ignition range more exact.Finally,the salience binarization region was extracted by the improved PCNN multiply iteration.Based on ASD standard data base,some experiments on 1 000 images were performed.The experimental results show that the proposed algorithm is superior to the five existing salience extraction algorithms uniformly in visual effect and objective quantitative data comparison.The results display that the precision ratio,recall ratio,and the overall F-measure of the proposed extraction algorithm are 0.891,0.808,and 0.870,respectively.In a real context experiment,the proposed algorithm gets more accurate extraction effect,which verifies that the proposed algorithm has higher accuracy and execution efficiency.