Principal Component Analysis-Based Visual Saliency Detection

In this paper, a novel patch-wise saliency detection algorithm is proposed based on principal component analysis (PCA). As a powerful statistical procedure in data analysis, PCA is fully exploited to convert color space and produce compact patch representation. Specifically, images are first converted to linearly uncorrelated channels and divided into non-overlapped patches. Then the patches are represented by the coefficients of principal components using PCA analysis. Based on the compact representation of patches, two types of distinctiveness are introduced: 1) center-surround contrast and 2) global rarity. Experimental results demonstrate that the PCA-based color space conversion and patch representation can improve the accuracy of human fixations prediction. And the proposed algorithm outperforms the mainstream algorithms on predicting human fixations. Additional experiments on salient object detection and image retargeting show that the proposed model can achieve better performance than traditional models.

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

[2]  Wenbin Zou,et al.  Saliency Tree: A Novel Saliency Detection Framework , 2014, IEEE Transactions on Image Processing.

[3]  A. Treisman,et al.  A feature-integration theory of attention , 1980, Cognitive Psychology.

[4]  Radomír Mech,et al.  Minimum Barrier Salient Object Detection at 80 FPS , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[5]  Harish Katti,et al.  An Eye Fixation Database for Saliency Detection in Images , 2010, ECCV.

[6]  Nicolas Riche,et al.  RARE2012: A multi-scale rarity-based saliency detection with its comparative statistical analysis , 2013, Signal Process. Image Commun..

[7]  Yu Fu,et al.  Visual saliency detection by spatially weighted dissimilarity , 2011, CVPR 2011.

[8]  Ju Liu,et al.  Image Adaptation and Dynamic Browsing Based on Two-Layer Saliency Combination , 2013, IEEE Transactions on Broadcasting.

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

[10]  A. Bovik,et al.  Visual search in noise: revealing the influence of structural cues by gaze-contingent classification image analysis. , 2006, Journal of vision.

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

[12]  Liming Zhang,et al.  A Novel Multiresolution Spatiotemporal Saliency Detection Model and Its Applications in Image and Video Compression , 2010, IEEE Transactions on Image Processing.

[13]  Nuno Vasconcelos,et al.  The discriminant center-surround hypothesis for bottom-up saliency , 2007, NIPS.

[14]  Asha Iyer,et al.  Components of bottom-up gaze allocation in natural images , 2005, Vision Research.

[15]  Antón García-Díaz,et al.  Saliency from hierarchical adaptation through decorrelation and variance normalization , 2012, Image Vis. Comput..

[16]  Huchuan Lu,et al.  Saliency Detection via Graph-Based Manifold Ranking , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[17]  Wenjun Zhang,et al.  Automatic Contrast Enhancement Technology With Saliency Preservation , 2015, IEEE Transactions on Circuits and Systems for Video Technology.

[18]  Peyman Milanfar,et al.  Static and space-time visual saliency detection by self-resemblance. , 2009, Journal of vision.

[19]  Tao Liu,et al.  Saliency Inspired Full-Reference Quality Metrics for Packet-Loss-Impaired Video , 2011, IEEE Transactions on Broadcasting.

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

[21]  Ali Borji,et al.  Quantitative Analysis of Human-Model Agreement in Visual Saliency Modeling: A Comparative Study , 2013, IEEE Transactions on Image Processing.

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

[23]  Ali Borji,et al.  State-of-the-Art in Visual Attention Modeling , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  S. Süsstrunk,et al.  Frequency-tuned salient region detection , 2009, CVPR 2009.

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

[26]  Laurent Itti,et al.  Automatic foveation for video compression using a neurobiological model of visual attention , 2004, IEEE Transactions on Image Processing.

[27]  Xiangyang Wang,et al.  Improving Saliency Detection Via Multiple Kernel Boosting and Adaptive Fusion , 2016, IEEE Signal Processing Letters.

[28]  Martin D. Levine,et al.  Saliency Detection Based on Frequency and Spatial Domain Analyses , 2011, BMVC.

[29]  Huang-Chia Shih,et al.  A Novel Attention-Based Key-Frame Determination Method , 2013, IEEE Transactions on Broadcasting.

[30]  D. M. Green,et al.  Signal detection theory and psychophysics , 1966 .

[31]  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.

[32]  Nanning Zheng,et al.  Learning to Detect a Salient Object , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[33]  Ariel Shamir,et al.  Improved seam carving for video retargeting , 2008, SIGGRAPH 2008.

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

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

[36]  Michael Dorr,et al.  Large-Scale Optimization of Hierarchical Features for Saliency Prediction in Natural Images , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[37]  Yin Li,et al.  Incremental sparse saliency detection , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

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

[39]  Esa Rahtu,et al.  Segmenting Salient Objects from Images and Videos , 2010, ECCV.

[40]  Guangtao Zhai,et al.  Visual saliency model based on minimum description length , 2016, 2016 IEEE International Symposium on Circuits and Systems (ISCAS).

[41]  Lihi Zelnik-Manor,et al.  Context-Aware Saliency Detection , 2012, IEEE Trans. Pattern Anal. Mach. Intell..

[42]  Marcus Barkowsky,et al.  The Importance of Visual Attention in Improving the 3D-TV Viewing Experience: Overview and New Perspectives , 2011, IEEE Transactions on Broadcasting.

[43]  Martin D. Levine,et al.  Visual Saliency Based on Scale-Space Analysis in the Frequency Domain , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[45]  Christof Koch,et al.  Image Signature: Highlighting Sparse Salient Regions , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[46]  Weisi Lin,et al.  Visual Saliency Detection With Free Energy Theory , 2015, IEEE Signal Processing Letters.

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

[48]  Ali Borji,et al.  Exploiting local and global patch rarities for saliency detection , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[49]  Jian Sun,et al.  Saliency Optimization from Robust Background Detection , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[50]  Yael Pritch,et al.  Saliency filters: Contrast based filtering for salient region detection , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

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

[52]  Yu-Wing Tai,et al.  Salient Region Detection via High-Dimensional Color Transform , 2014, CVPR.