融合显著深度特征的RGB-D图像显著目标检测

吴建国; 邵婷; 刘政怡*

doi:10.11999/JEIT161304

融合显著深度特征的RGB-D图像显著目标检测

doi: 10.11999/JEIT161304

基金项目:

国家科技支撑计划(2015BAK24B00)，高等学校博士学科点专项科研基金(20133401110009)，安徽高校省级自然科学研究项目(KJ2015A009)，安徽大学信息保障技术协同创新中心开放课题

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出版历程
- 收稿日期: 2016-12-08
- 修回日期: 2017-05-22
- 刊出日期: 2017-09-19

RGB-D Saliency Detection Based on Integration Feature of Color and Depth Saliency Map

Funds:

The National Key Technology RD Program (2015BAK24B00), The Specialized Research Fund for the Doctoral Program of Higher Education of China (20133401110009), Key Program of Natural Science Project of Educational Commission of Anhui Province (KJ2015A009), Open Funds of Co-Innovation Center for Information Supply Assurance Technology of Anhui University

摘要

摘要: 深度信息被证明是人类视觉的重要组成部分，然而大部分显著性检测工作侧重于2维图像上的方法，并不能很好地利用深度进行RGB-D图像显著性检测。该文提出一种融合显著深度特征的RGB-D图像显著目标检测方法，提取基于颜色和深度显著图的综合特征，根据构图先验和背景先验的方法进行显著目标检测。首先，对原始深度图进行预处理：使用背景顶点区域、构图交点和紧密度处理深度图，多角度融合形成深度显著图，并作为显著深度特征，结合颜色特征形成综合特征；其次，从前景角度，将综合特征通过边连接权重构造关联矩阵，根据构图先验，假设多层中心矩形为前景种子，通过流形排序方法计算出RGB-D图像的前景显著图；从背景角度，根据背景先验以及边界连通性计算出背景显著图；最后，将前景显著图和背景显著图进行融合并优化得到最终显著图。实验采用RGB-D1000数据集进行显著性检测，并与4种不同的方法进行对比，所提方法的显著性检测结果更接近人工标定结果，PR(查准率-查全率)曲线显示在相同召回率下准确率高于其他方法。
- 显著目标检测 /
- 显著深度特征 /
- 多层中心矩形 /
- 流形排序 /
- 构图先验 /
- 背景先验
Abstract: Depth information is proved to be an important part of human vision. However, most saliency detection methods based on 2D images do not make good use of depth information, thus an effective saliency detection method for RGB-D image is presented. It extracts color feature combined with depth saliency feature and detects salient objects based on photographic composition prior and background prior. First, original depth map is preprocessed to form depth saliency feature by background vertex area, photographic composition intersections, and compactness method. Then the association matrix is constructed by the adjacency weight of comprehensive feature. Manifold ranking is running from foreground view to form foreground saliency map based on photographic composition prior and fusion of depth saliency feature and color feature. In order to correct the error caused by assumption, the boundary connectivity is used to suppress background from background view. Final saliency map builds on fusion of foreground and background saliency map. Experiments compared with 4 different methods on RGB-D1000 database show that the proposed method has better precision-recall curve and outperforms the state- of-the-art methods.
- Salient object detection /
- Depth saliency map /
- Multi-layer center rectangle /
- Manifold ranking /
- Photographic composition prior /
- Background prior

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参考文献(23)

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