Saliency detection based on global and local short-term sparse representation

Saliency detection has been considered to be an important issue in many computer vision tasks. In this paper, we propose a novel bottom-up saliency detection method based on sparse representation. Saliency detection includes two elements: image representation and saliency measurement. For an input image, first, the ICA algorithm is employed to learn a set of basis functions, then the image can be represented by this set of basis functions. Next, a global and local saliency framework is employed to measure the saliency. The global saliency is obtained through Low-Rank Representation (LRR), and the local saliency is obtained through a sparse coding scheme. The proposed method is compared 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.*Corresponding author.

[1]  Derrick J. Parkhurst,et al.  Modeling the role of salience in the allocation of overt visual attention , 2002, Vision Research.

[2]  Tim K Marks,et al.  SUN: A Bayesian framework for saliency using natural statistics. , 2008, Journal of vision.

[3]  Christof Koch,et al.  A Model of Saliency-Based Visual Attention for Rapid Scene Analysis , 2009 .

[4]  Liming Zhang,et al.  Spatio-temporal Saliency detection using phase spectrum of quaternion fourier transform , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[5]  Mubarak Shah,et al.  Visual attention detection in video sequences using spatiotemporal cues , 2006, MM '06.

[6]  Nicu Sebe,et al.  Image saliency by isocentric curvedness and color , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[7]  Guillermo Sapiro,et al.  Online Learning for Matrix Factorization and Sparse Coding , 2009, J. Mach. Learn. Res..

[8]  Nanning Zheng,et al.  Learning to Detect A Salient Object , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Shi-Min Hu,et al.  Global contrast based salient region detection , 2011, CVPR 2011.

[10]  King Ngi Ngan,et al.  Unsupervised extraction of visual attention objects in color images , 2006, IEEE Transactions on Circuits and Systems for Video Technology.

[11]  Laurent Itti,et al.  An Integrated Model of Top-Down and Bottom-Up Attention for Optimizing Detection Speed , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[12]  Patrick Le Callet,et al.  A coherent computational approach to model bottom-up visual attention , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  S. Avidan,et al.  Seam carving for content-aware image resizing , 2007, SIGGRAPH 2007.

[14]  Christof Koch,et al.  Learning a saliency map using fixated locations in natural scenes. , 2011, Journal of vision.

[15]  K. Fujii,et al.  Visualization for the analysis of fluid motion , 2005, J. Vis..

[16]  David J. Field,et al.  Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.

[17]  Gert Kootstra,et al.  Paying Attention to Symmetry , 2008, BMVC.

[18]  Byoung Chul Ko,et al.  Object-of-interest image segmentation based on human attention and semantic region clustering. , 2006, Journal of the Optical Society of America. A, Optics, image science, and vision.

[19]  Liqing Zhang,et al.  Dynamic visual attention: searching for coding length increments , 2008, NIPS.

[20]  Pietro Perona,et al.  Graph-Based Visual Saliency , 2006, NIPS.

[21]  Mark Goadrich,et al.  The relationship between Precision-Recall and ROC curves , 2006, ICML.

[22]  Hao Zhu,et al.  Bottom-up saliency based on weighted sparse coding residual , 2011, ACM Multimedia.

[23]  Laurent Itti,et al.  Realistic avatar eye and head animation using a neurobiological model of visual attention , 2004, SPIE Optics + Photonics.

[24]  Touradj Ebrahimi,et al.  Christopoulos: Thc Jpeg2000 Still Image Coding System: an Overview the Jpeg2000 Still Image Coding System: an Overview , 2022 .

[25]  Yong Yu,et al.  Robust Subspace Segmentation by Low-Rank Representation , 2010, ICML.

[26]  Junchi Yan,et al.  Visual Saliency Detection via Sparsity Pursuit , 2010, IEEE Signal Processing Letters.

[27]  HongJiang Zhang,et al.  Contrast-based image attention analysis by using fuzzy growing , 2003, MULTIMEDIA '03.

[28]  R. Tibshirani,et al.  Least angle regression , 2004, math/0406456.

[29]  Bernhard Schölkopf,et al.  A Nonparametric Approach to Bottom-Up Visual Saliency , 2006, NIPS.

[30]  Frédo Durand,et al.  Learning to predict where humans look , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[31]  Rongrong Ji,et al.  Saliency detection based on short-term sparse representation , 2010, 2010 IEEE International Conference on Image Processing.

[32]  John K. Tsotsos,et al.  Saliency Based on Information Maximization , 2005, NIPS.

[33]  S Ullman,et al.  Shifts in selective visual attention: towards the underlying neural circuitry. , 1985, Human neurobiology.

[34]  Benjamin B. Bederson,et al.  Automatic thumbnail cropping and its effectiveness , 2003, UIST '03.

[35]  Pierre Baldi,et al.  Bayesian surprise attracts human attention , 2005, Vision Research.

[36]  Liqing Zhang,et al.  Saliency Detection: A Spectral Residual Approach , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[37]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .

[38]  D H HUBEL,et al.  RECEPTIVE FIELDS AND FUNCTIONAL ARCHITECTURE IN TWO NONSTRIATE VISUAL AREAS (18 AND 19) OF THE CAT. , 1965, Journal of neurophysiology.

[39]  Pietro Perona,et al.  Is bottom-up attention useful for object recognition? , 2004, CVPR 2004.

[40]  D. Donoho.  For most large underdetermined systems of equations, the minimal 𝓁1‐norm near‐solution approximates the sparsest near‐solution , 2006 .