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　1000　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.　©,　2015,　Chinese　Academy　of　Sciences.　All　right　reserved.