显著中心先验和显著-深度概率矫正的RGB-D显著目标检测

刘政怡; 黄子超; 张志华

doi:10.11999/JEIT170235

显著中心先验和显著-深度概率矫正的RGB-D显著目标检测

doi: 10.11999/JEIT170235

基金项目:

国家科技支撑计划(2015BAK24B00)，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: 2017-03-20
- 修回日期: 2017-07-04
- 刊出日期: 2017-12-19

RGB-D Saliency detection Based on Saliency Center Prior and Saliency-depth Probability Adjustment

Funds:

The National Key Technology RD Program of the Ministry of Science and Technology of China (2015BAK24B00), The Key Program of Natural Science Project of Educational Commission of Anhui Province (KJ2015A009), The Open Issues on Co-Innovation Center for Information Supply Assurance Technology, Anhui University

摘要

摘要: 随着深度特征在图像显著检测领域中发挥越来越重要的作用，传统的RGB图像显著检测模型由于未能充分利用深度信息已经不能适用于RGB-D图像的显著检测。该文提出显著中心先验和显著-深度(S-D)概率矫正的RGB-D显著检测模型，使得深度特征和RGB特征间相互指导，相互补充。首先，依据3维空间权重和深度先验获取深度图像初步显著图；其次，采用特征融合的流形排序算法获取RGB图像的初步显著图。接着，计算基于深度的显著中心先验，并以该先验作为显著权重进一步提升RGB图像的显著检测结果，获取RGB图像最终显著图；再次，计算显著-深度矫正概率，并对深度图的初步显著检测结果使用此概率进行矫正。接着，计算基于RGB的显著中心先验，并以该先验作为显著权重进一步提升深度图像矫正后的显著检测结果，获取深度图像的最终显著图；最后，采用优化框架对深度图像最终显著图进行优化得到RGB-D图像最终的显著图。所有的对比实验都是在公开的数据集NLPR RGBD-1000数据集上进行，实验结果显示该文算法较当前流行的算法有更好的性能。
- 3维空间权重 /
- 特征融合的流形排序算法 /
- 显著中心先验 /
- 显著-深度概率矫正
Abstract: Along with more and more important role of depth features played in computer saliency community, traditional RGB saliency models can not directly utilized for saliency detection on RGB-D domains. This paper proposes saliency center prior and Saliency-Depth (S-D) probability adjustment RGB-D saliency detection framework, making the depth and RGB features adaptively fuse and complementary to each other. First, the initial saliency maps of depth images are obtained according to three-dimension space weights and depth prior; second, the feature fused Manifold Ranking model with extracted depth features is utilized for RGB image saliency detection. Then, the saliency center prior based on depth is computed and this value is used as saliency weight to further improve the RGB image saliency detection results, obtaining the final RGB saliency map. After that, Saliency-Depth (S-D) rectify probability is also computed and the saliency results of depth images are corrected with this probability. Then the saliency center prior based on RGB is also computed and this value is used as saliency weights to further improve the depth image saliency detection results and to obtain the final depth saliency maps. Finally the optimization framework is utilized to optimize the depth image final saliency maps and to obtain the final RGB-D saliency map. All the experiments are executed on the public NLPR RGBD-1000 benchmark and extensive experiments demonstrate that the proposed algorithm achieves better performance compared with existing state-of-the-art approaches.
- Three-dimension space weights /
- Feature fused driven manifold ranking /
- Saliency center prior /
- Saliency- Depth (S-D) probability adjustment

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