基于字典和加权低秩恢复的显著目标检测

摘要/Abstract

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。

关键词: 背景先验, 加权低秩恢复, 字典, 自适应系数

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.

Key words: Adaptive coefficient, Background prior, Dictionary, Weighted low-rank recovery

中图分类号:

TP391

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(6A): 146-150. https://doi.org/

MA Xiao-di, WU Xi-yin, JIN Zhong. Salient Object Detection Based on Dictionary and Weighted Low-rank Recovery[J]. Computer Science, 2018, 45(6A): 146-150. https://doi.org/

参考文献

[1]ITTI L,KOCH C,NIEBUR E.A Model of Saliency-Based Visual Attention for Rapid Scene Analysis[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1998,20(11):1254-1259.
[2]ALEXE B,DESELAERS T,FERRARI V.Measuring the Ob- jectness of Image Windows[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2012,34(11):2189-2202.
[3]CHANG K Y,LIU T L,LAI S H.From Co-saliency to Co-segmentation:An Efficient and Fully Unsupervised Energy Minimization Model[C]∥IEEE Conference on Computer Vision and Pattern Recognition.2011:2129-2136.
[4]GUO C,ZHANG L.A Novel Multiresolution Spatiotemporal Saliency Detection Model and its Applications in Image and Vi-deo Compression[J].IEEE Transactions on Image Processing,2010,19(1):185-198.
[5]SIAGIAN C,ITTI L.Rapid Biologically-Inspired Scene Classification Using Features Shared with Visual Attention[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2007,29(2):300-312.
[6]ITTI L,SIHITE D N,BORJI A.Probabilistic Learning of Task-Specific Visual Attention[C]∥IEEE Conference on Computer Vision and Pattern Recognition.2012:470-477.
[7]BORJI A.Exploiting Local and Global Patch Rarities for Salie- ncy Detection[C]∥IEEE Conference on Computer Vision and Pattern Recognition.2012:478-485.
[8]MEUR O L,CHEVET J C.Relevance of a Feed-Forward Model of Visual Attention for Goal-Oriented and Free-Viewing Tasks[J].IEEE Transactions on Image Processing,2010,19(11):2801-2813.
[9]WRIGHT J,PENG Y,MA Y,et al.Robust Principal Component Analysis:Exact Recovery of Corrupted Low-Rank Matrices by Convex Optimization[C]∥International Conference on Neural Information Processing Systems.2009:2080-2088.
[10]TANG C,WANG P,ZHANG C,et al.Salient Object Detection via Weighted Low Rank Matrix Recovery[J].IEEE Signal Processing Letters,2017,24(4):490-494.
[11]LIU G,LIN Z,YAN S,et al.Robust Recovery of Subspace Structures by Low-Rank Representation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2013,35(1):171-184.
[12]SHEN X,WU Y.A Unified Approach to Salient Object Detection via Low Rank Matrix Recovery[C]∥IEEE Conference on Computer Vision and Pattern Recognition.2012:853-860.
[13]LANG C,LIU G,YU J,et al.Saliency Detection by Multitask Sparsity Pursuit[J].IEEE Transactions on Image Processing,2012,21(3):1327-1338.
[14]Li N,SUN B,YU J.A Weighted Sparse Coding Framework for Saliency Detection[C]∥IEEE Conference on Computer Vision and Pattern Recognition.2015:5216-5223.
[15]ACHANTA R,SHAJI A,SMITH K,et al.SLIC Superpixels Compared to State-of-the-art Superpixel Methods[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2012,34(11):2274-2282.
[16]ZHU W,LIANG S,WEI Y,et al.Saliency Optimization from Robust Background Detection[C]∥IEEE Conference on Computer Vision and Pattern Recognition.2014:2814-2821.
[17]CHEN M,LIN Z,MA Y,et al.The Augmented Lagrange Multiplier Method for Exact Recovery of Corrupted Low-Rank Matrices[J].Eprint Arxiv,2010:1-23.
[18]CAI J F,CAND S E J,SHEN Z.A Singular Value Thresholding Algorithm for Matrix Completion[J].Siam Journal on Optimization,2008,20(4):1956-1982.
[19]LIU G,LIN Z,YU Y.Robust Subspace Segmentation by Low-Rank Representation[C]∥International Conference on Machine Learning.2010:663-670.
[20]BERTSEKAS D P.Constrained Optimization and Lagrange Multiplier Methods[M].New York:Academic Press,1982.
[21]AFONSO M V,BIOUCAS-DIAS J M,FIGUEIREDO M A T.An Augmented Lagrangian Approach to Linear Inverse Problems with Compound Regularization[C]∥IEEE Conference on Image Processing.2010:4169-4172.
[22]ECKSTEIN J,BERTSEKAS D P.On the Douglas-Rachford Splitting Method and the Proximal Point Algorithm for Maximal Monotone Operators[J].Mathematical Programming,1992,55(1):293-318.
[23]CHENG M M,MITRA N J,HUANG X,et al.Global Contrast Based Salient Region Detection[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2015,37(3):569-582.
[24]MOVAHEDI V,ELDER J H.Design and Perceptual Validation of Performance Measures for Salient Object Segmentation[C]∥Conference on Computer Vision and Pattern Recognition Workshops.2010:49-56.
[25]YAN Q,XU L,SHI J,et al.Hierarchical Saliency Detection[C]∥ IEEE Conference on Computer Vision and Pattern Recognition.2013:1155-1162.
[26]LI Y,HOU X,KOCH C,et al.The Secrets of Salient Object Segmentation[C]∥IEEE Conference on Computer Vision and Pattern Recognition.2014:280-287.
[27]ZHAI Y,SHAH M.Visual Attention Detection in Video Sequences Using Spatiotemporal Cues[C]∥ACM International Conference on Multimedia.2006:815-824.
[28]ACHANTA R,HEMAMI S,ESTRADA F,et al.Frequency- Tuned Salient Region Detection[C]∥IEEE Conference on Computer Vision and Pattern Recognition.2009:1597-1604.
[29]HOU X,ZHANG L.Saliency Detection:A Spectral Residual Approach[C]∥IEEE Conference on Computer Vision and Pattern Recognition.2007:1-8.
[30]YANG C,ZHANG L,LU H,et al.Saliency Detection via Graph-Based Manifold Ranking[C]∥IEEE Conference on Computer Vision and Pattern Recognition.2013:3166-3173.
[31]GONG C,TAO D,LIU W,et al.Saliency Propagation from Simple to Difficult[C]∥IEEE Conference on Computer Vision and Pattern Recognition.2015:2531-2539.

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