计算机工程与应用2018，Vol.54Issue(8)：172-177,6.DOI:10.3778/j.issn.1002-8331.1610-0159
融合多尺度对比与贝叶斯模型的显著目标检测
Salient object detection method based on multi-scale contrast and Bayesian model
摘要
Abstract
In order to overcome the problems of nonuniform in salient object highlight and weak ability of anti-background existing in traditional approaches of saliency detection,a salient object detection method based on multi-scale contrast and Bayesian model is proposed.Firstly,the source image is segmented into a series of superpixels with same color feature, and multi-scale segmentation maps are calculated by K-means algorithm. Secondly, background priors and convex-hull center prior computing multi-scale saliency maps are adopted, and then a coarse saliency map is calculated through weighted summation.Finally,based on the rough region,a prior map is computed for the Bayesian model to achieve the final saliency map.Compared with 6 state-of the-art methods on publicly available datasets(MSRA-1000),the simulation results demonstrate that the salient object detection approach proposed in this paper performs more uniform in highlight salient object, with higher precision ratio and lower mean absolute error.关键词
多尺度/贝叶斯模型/背景先验/显著目标Key words
multi-scale/Bayesian model/background priors/salient object分类
信息技术与安全科学引用本文复制引用
邓晨,谢林柏..融合多尺度对比与贝叶斯模型的显著目标检测[J].计算机工程与应用,2018,54(8):172-177,6.基金项目
国家自然科学基金(No.61374047,No.60973095). （No.61374047,No.60973095）
