摘要
为了更鲁棒地检测图像中的显著目标.在凸包的基础上,提出一种基于自适应遗传算法的显著性检测算法.首先通过图像的Harris角点构造凸包,利用自适应遗传算法来找出凸包内的显著目标并构造遗传先验图;然后构建中心先验模型,与遗传先验图融合成先验图;最后引入贝叶斯优化框架来优化先验图,以得到最终的显著图.在6个公开的显著性检测数据库上进行评测,通过大量实验验证了该算法的有效性.
In order to more robustly detect the salient targets in images,based on the convex hull,our paper proposed a saliency detection algorithm based on adaptive genetic algorithm.Firstly,the convex hull is constructed by Harris points of the image,the adaptive genetic algorithm is used to find the salient areas of convex hull and construct the genetic prior map.Then,construct center prior model and integrate genetic prior map and center prior model into a prior map;use Bayesian optimization framework to optimize the prior map to obtain the final saliency map.Our algorithm is evaluated on the six open saliency dataset.Extensive experimental results demonstrate that our algorithm is effective.
作者
蒋林华
钟荟
林晓
马利庄
Jiang Linhua;Zhong Hui;Lin Xiao;Ma Lizhuang(School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093;The College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 200234;College of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240)
出处
《计算机辅助设计与图形学学报》
EI
CSCD
北大核心
2017年第11期1971-1979,共9页
Journal of Computer-Aided Design & Computer Graphics
基金
国家自然科学基金青年项目(61502220)
国家自然科学基金联合项目(U1304616)
国家自然科学基金面上项目(61775139)
上海市自然科学基金(15ZR1428600)
关键词
遗传算法
中心先验
显著性
贝叶斯框架
genetic algorithm
center prior
saliency
Bayesian framework
