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清华大学学报(自然科学版)  2017, Vol. 57 Issue (1): 72-78    DOI: 10.16511/j.cnki.qhdxxb.2017.21.014
  电子工程 本期目录 | 过刊浏览 | 高级检索 |
基于边界扩展的图像显著区域检测
刘杰1,2,3, 王生进1,2,3
1. 清华大学 电子工程系, 北京 100084;
2. 智能技术与系统国家重点实验室, 北京 100084;
3. 清华大学信息技术国家实验室, 北京 100084
Image salient region detection based on boundary expansion
LIU Jie1,2,3, WANG Shengjin1,2,3
1. Department of Electronic Engineering, Tsinghua University, Beijing 100084, China;
2. State Key Laboratory of Intelligent Technology and Systems, Beijing 100084, China;
3. Tsinghua National Laboratory for Information Science and Technology, Beijing 100084, China
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摘要 在显著区域检测中,背景先验已被证明有效。通常,图像的边界图像块被假设为背景,其他图像块根据与边界图像块之间的差异来确定显著性,差异越大则显著性越强。然而,当图像背景杂乱或者前景与图像边界有重叠时,仅仅利用边界图像块作为背景将会产生包含较强噪声的显著图,从而使得检测精度下降。该文首先将图像边界图像块向图像内部扩展,使其包含尽可能多的背景像素;然后,利用未扩展到的图像块作为前景查询项,采用二级排序算法来度量所有图像块的显著性。在3个公开的复杂显著区域检测数据集上的大量实验表明该算法优于其他5种算法。
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刘杰
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关键词 显著区域检测边界扩展流形排序相异性测度    
Abstract:Background priors have been shown to improve salient region detection. Typically, image boundary patches are assumed to be the background and the saliency of other patches is defined by their difference from the boundaries. A greater difference indicates a more salient patch. However, when the background is cluttered, or the foreground overlaps the image boundary, using only boundary patches to indicate the background may lead to a saliency map with strong noise and compromise the detection accuracy. To address this problem, the boundary patches are first expanded here into the image interior to contain as much background as possible. Then, the rest of the patches are used as foreground queries with the saliency of each patch measured by a two-stage ranking algorithm. Tests on three large public datasets demonstrate the superiority of this method over five other algorithms.
Key wordssalient region detection    boundary expansion    manifold ranking    dissimilarity measure
收稿日期: 2016-06-02      出版日期: 2017-01-20
ZTFLH:  TP399  
通讯作者: 王生进,教授,E-mail:wgsgj@tsinghua.edu.cn     E-mail: wgsgj@tsinghua.edu.cn
引用本文:   
刘杰, 王生进. 基于边界扩展的图像显著区域检测[J]. 清华大学学报(自然科学版), 2017, 57(1): 72-78.
LIU Jie, WANG Shengjin. Image salient region detection based on boundary expansion. Journal of Tsinghua University(Science and Technology), 2017, 57(1): 72-78.
链接本文:  
http://jst.tsinghuajournals.com/CN/10.16511/j.cnki.qhdxxb.2017.21.014  或          http://jst.tsinghuajournals.com/CN/Y2017/V57/I1/72
  图1 基于背景先验的传统显著区域检测算法存在的问题
  图2 基于边界扩展的显著区域检测算法框架
  图3 随着像素位置的变化,内部像素与边界背景像素之间的颜色差异
  图4 背景像素所占比例与像素位置的关系
  图5 MSRA10K数据集上不同显著区域检测算法的结果对比
  图6 ECSSD数据集上不同显著区域检测算法的结果对比
  图7 DUT-OMRON 数据集上不同显著区域检测算法结果对比
  表1 不同算法自适应阈值分割结果
  表2 不同算法在3个数据集上的MAE指标
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[1] 刘杰, 王生进. 融合聚类与排序的图像显著区域检测[J]. 清华大学学报(自然科学版), 2016, 56(9): 913-919.
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