| 引用本文: | 陈炳才,余超,周超,陶鑫,高振国.水平集超像素及贝叶斯框架下的显著性检测[J].哈尔滨工业大学学报,2018,50(5):102.DOI:10.11918/j.issn.0367-6234.201709032 |
| CHEN Bingcai,YU Chao,ZHOU Chao,TAO Xin,GAO Zhenguo.Saliency detection based on level set superpixels and Bayesian framework[J].Journal of Harbin Institute of Technology,2018,50(5):102.DOI:10.11918/j.issn.0367-6234.201709032 |
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| 摘要: |
| 针对数字图像显著性检测过程中对超像素的分割及相应显著值的计算不准确问题，提出了一种基于水平集超像素和贝叶斯框架的数字图像显著性检测和更新算法.首先，对基于灰度不均匀的水平集方法的结果先进行分割合并操作，可以得到适应图像不同区域大小的水平集超像素.其次，使用图像内部与边缘超像素之间的颜色和距离差异来构建显著性图.接着，使用水平集超像素来表示显著区域，以图像边缘部分的超像素为基础，基于K均值聚类算法并在贝叶斯框架下提出三种更新算法，用来更新显著性图从而得到显著性结果；更新算法可以进一步提高显著图的准确率、召回率、F值这3个指标，降低平均绝对误差.最后，提出了基于人脸识别的检测算法来处理包含有人的图片.在三个公开的数据库上进行了定性和定量的大量实验评测，结果表明本文提出的显著性检测方法和更新算法在准确率、召回率、F值及平均绝对误差这四个指标上均优于FT、CA、XL、MR、wCO、BSCA等已有的图像显著性检测经典算法.
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| 关键词: 显著性检测 水平集超像素 贝叶斯框架 人脸识别 |
| DOI：10.11918/j.issn.0367-6234.201709032 |
| 分类号:TP391 |
| 文献标识码:A |
| 基金项目:国家自然科学基金(9,2, 61671169) |
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| Saliency detection based on level set superpixels and Bayesian framework |
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CHEN Bingcai1,2,YU Chao1,ZHOU Chao1,TAO Xin1,GAO Zhenguo1
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(1. Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China; 2. College of Computer Science and Technology, Xinjiang Normal University, Wulumuqi 830054, China)
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| Abstract: |
| Aiming at the inaccuracy of superpixel segmentation and the value calculation of saliency map in image saliency detection, this paper proposes an alternative form of saliency detection and updated algorithm based on the level set superpixels and Bayesian framework. A division or merger of the superpixels will achieve a level set method to form superpixels which are adaptive to the different regions of different size. The model's original map will be constructed by using color and space contrast between boundary and inner superpixels. The saliency region can be indicated with the level set superpixels and then three updated algorithms are effectively put forward via the Bayesian framework and based on K-means clustering algorithm. As a result of this update to the original mapping system, the final saliency result can be discovered in a manner that is a vast improvement of the existing method while still achieving a better level. Using our algorithm can improve the accuracy, recall rate, F value and reduce the mean absolute error value. Finally, this saliency detection algorithm for the face recognition can be effectively utilized to deal with the pictures containing people. The experimental results on three open databases show that our proposed algorithm is superior to FT, CA, XL, MR, wCO, BSCA and other saliency detection algorithms in accuracy, recall rate, F value and mean absolute error.
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| Key words: saliency detection level set superpixel Bayesian framework face recognition |
