| 基于字典和加权低秩恢复的显著目标检测 |
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| 引用本文： | 马晓迪,吴茜茵,金忠.基于字典和加权低秩恢复的显著目标检测[J].计算机科学,2018,45(Z6):146-150, 161. |
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| 作者姓名： | 马晓迪 吴茜茵 金忠 |
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| 作者单位： | 南京理工大学计算机科学与工程学院 南京210094 南京理工大学高维信息智能感知与系统教育部重点实验室 南京210094,南京理工大学计算机科学与工程学院 南京210094 南京理工大学高维信息智能感知与系统教育部重点实验室 南京210094,南京理工大学计算机科学与工程学院 南京210094 南京理工大学高维信息智能感知与系统教育部重点实验室 南京210094 |
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| 基金项目： | 本文受国家自然科学基金(61602244,2,61602444,1,61472187),国家重点基础研究发展计划(2014CB349303),国家预研领域基金(6140312010101)资助 |
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| 摘 要： | 显著目标检测旨在辨别出自然图像中的显著区域。为了提高检测效果,提出了基于字典和加权低秩恢复的显著目标检测。首先,在低秩恢复模型中融入字典,以更好地将低秩矩阵和稀疏矩阵分离；然后,获取颜色、位置和边界连接先验对应的稀疏矩阵,根据其显著值生成先验系数；最后,将3个先验用自适应系数组合的方式构造权重矩阵,并融入到低秩恢复模型中。在4个具有挑战性的数据集上将其与11种算法进行比较,实验结果表明,所提算法的效果最好。
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| 关 键 词： | 字典 背景先验 加权低秩恢复 自适应系数 |
Salient Object Detection Based on Dictionary and Weighted Low-rank Recovery |
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| MA Xiao-di,WU Xi-yin and JIN Zhong.Salient Object Detection Based on Dictionary and Weighted Low-rank Recovery[J].Computer Science,2018,45(Z6):146-150, 161. |
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| Authors: | MA Xiao-di WU Xi-yin and JIN Zhong |
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| Affiliation: | School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing 210094,China Key Laboratory of Intelligent Perception and System for High-Dimensional Information of Ministry of Education, Nanjing University of Science and Technology,Nanjing 210094,China,School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing 210094,China Key Laboratory of Intelligent Perception and System for High-Dimensional Information of Ministry of Education, Nanjing University of Science and Technology,Nanjing 210094,China and School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing 210094,China Key Laboratory of Intelligent Perception and System for High-Dimensional Information of Ministry of Education, Nanjing University of Science and Technology,Nanjing 210094,China |
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| Abstract: | Salient object detection intends to identify salient areas in natural images.In order to improve detection results,a method based on dictionary and weighted low-rank recovery for salient object detection was proposed.Firstly,a dictionary is incorporated into the low rank recovery model to separate the low rank matrix from the sparse matrix better.Secondly,sparse matrices corresponding to the color,location and boundary connectivity priors are obtained,and the adaptive coefficients are generated by their saliency values.Finally,a weighted matrix is constructed by adaptive coefficients with three priors,and the matrix is merged into the low rank recovery model.Compared with eleven state-of-the-art methods in four challenging databases,the experiment results show that the proposed approach outperforms the state-of-the-art solutions. |
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| Keywords: | Dictionary Background prior Weighted low-rank recovery Adaptive coefficient |
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