<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">LiJia(博士后)；*TianYonghong；DuanLingyu；HuangTiejun</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Estimating Visual Saliency Through Single Image Optimization</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Signal Processing Letters</style></secondary-title><short-title><style face="normal" font="default" size="100%">Estimating Visual Saliency Through Single Image Optimization</style></short-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Quadratic programming; single image optimization; visual saliency</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2013/9</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">20</style></volume><pages><style face="normal" font="default" size="100%">845-848</style></pages><isbn><style face="normal" font="default" size="100%">1070-9908</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This letter presents a novel approach for visual saliency estimation through single image optimization. Instead of directly mapping visual features to saliency values with a unified model, we treat regional saliency values as the optimization objective on each single image. By using a quadratic programming framework, our approach can adaptively optimize the regional saliency values on each specific image to simultaneously meet multiple saliency hypotheses on visual rarity, center-bias and mutual correlation. Experimental results show that our approach can outperform 14 state-of-the-art approaches on a public image benchmark.</style></abstract><issue><style face="normal" font="default" size="100%">9</style></issue><custom7><style face="normal" font="default" size="100%">000321338100001</style></custom7><section><style face="normal" font="default" size="100%">845</style></section></record></records></xml>