Computer Science ›› 2014, Vol. 41 ›› Issue (10): 80-83.doi: 10.11896/j.issn.1002-137X.2014.10.018

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Saliency Detection Based on Global and Local Short-term Sparse Representation

FAN Qiang and QI Chun   

  • Online:2018-11-14 Published:2018-11-14

Abstract: Saliency detection has been considered to be an important issue in many computer vision tasks.We proposed a novel bottom-up saliency detection method based on sparse representation.Saliency detection includes two elements:image representation and saliency measurement.The two elements used in our method are both biological plausible and accurate.For an input image,we first used ICA algorithm to learn a set of basis functions,then the image could be represented by the set of basis functions.Next,we used a global and local saliency framework to measure the saliency respectively,and combined the two results to obtain the final saliency.The global saliency is obtained through Low-Rank Representation(LRR),and the local saliency is obtained through a sparse coding scheme.We compared our method with six state-of-the-art methods on two popular human eye fixation datasets.The experimental results indicate the accuracy of the proposed method to predict the human eye fixations is higher.

Key words: Saliency detection,Sparse representation,Low-rank representation,Sparse coding

[1] Avidan S,Shamir A.Seam carving for content-aware image resizing[J].ACM Transactions on Graphics,2007,26(3):10
[2] Han J,Ngan K,Li M,et al.Unsupervised extraction of visual attention objects in color images[J].IEEE Transactions on Circuits and Systems for Video Technology,2006,16(1):141-145
[3] Ko B,Nam J.Object-of-interest image segmentation based onhuman attention and semantic region clustering[J].JOSA.A,2006,23(10):2462-2470
[4] Rutishauser U,Walther D,Koch C,et al.Is bottom-up attention useful for object recognition? [C]∥Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.2004,2:37-44
[5] Itti U,Koch C,Niebur E.A model of saliency-based visual attention for rapid scene analysis[J].IEEE Transaction on Pattern Analysis and Machine Intelligence,1998,20(11):1254-1259
[6] Judd T,Ehinger K,Durand F,et al.Learning to predict where humans look[C]∥IEEE International Conference on Computer Vision.2009:2106-2113 (下转第116页)(上接第83页)
[7] Olshausen B,Field D.Emergence of simple-cell receptive fieldproperties by learning a sparse code for natural images[J].Natural,1996,381(6583):607-609
[8] Bruce N,Tsotsos J.Saliency based on information maximization[C]∥Advances in neural information processing systems.2006,18:155-162
[9] Hou X,Zhang L.Dynamic visual attention:Searching for coding length increments[C]∥Advancesin Neural Information Proces-sing Systems.2008,21:681-688
[10] Yan J,Zhu M,Liu H.Visual saliency detection via sparsity pursuit[J].IEEE Signal Processing Letters,2010,17(8):739-742
[11] Sun X,Yao H,Ji R.Saliency detection based on short-termsparse representation[C]∥Proceedings of 2010 IEEE 17th International Conference on Image Processing.2010:1101-1104
[12] Harel J,Koch C,Perona P.Graph-based visual saliency[C]∥Advances in Neural Information Processing System.2006:545-552
[13] Vinje W,Gallant J.Receptive fields and functional architecture in two nonstriate visual areas[J].Journal of Nerophysiol,1965,28:229-289
[14] Liu G,Lin Z,Yu Y.Robust subspace segmentation by low-rank representation[C]∥International Conference on machine lear-ning.2010
[15] Liu G,Chen M,Ma Y.The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrix[R].UTUC Technical Report UILU-ENG-09-2215.2009
[16] Han B,Zhu H,Ding Y.Bottom up saliency based on weightedsparse coding residual[C]∥Proceedings of the 19th Annual ACM International Conference on Multimedia.2011:1117-1120
[17] Donoho D L.For most large underdetermined systems of linearequations the minimal[J].Communications on pure and applied mathematics,2006,59(6):797-829
[18] Tibshirani R.Regression shrinkage and selection via the lasso[J].Journal of the Royal Statistical Society Series B(Methodological),1996,58(1):267-288
[19] Mairal J,Bach F,Ponce J,et al.Online learning for matrix factorization and sparse coding[J].The Journal of Machine Lear-ning Research,2010,11:19-60
[20] Borji A,Itti L.Exploiting local and global patch rarieties for saliency detection[C]∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.2012:478-485
[21] Davis J,Goadrich M.The relationship between Precision-Recall and ROC curves[C]∥Proceedings of the 23th International Conference on Machine Learning.2006:233-240
[22] Hou X,Zhang L.Saliency detection:A spectral residual ap-proach[C]∥IEEE International conference on Computer Vision and Pattern Recognition.2007:1-8

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