Exploiting Surroundedness for Saliency Detection: A Boolean Map Approach

We demonstrate the usefulness of surroundedness for eye fixation prediction by proposing a Boolean Map based Saliency model (BMS). In our formulation, an image is characterized by a set of binary images, which are generated by randomly thresholding the image's feature maps in a whitened feature space. Based on a Gestalt principle of figure-ground segregation, BMS computes a saliency map by discovering surrounded regions via topological analysis of Boolean maps. Furthermore, we draw a connection between BMS and the Minimum Barrier Distance to provide insight into why and how BMS can properly captures the surroundedness cue via Boolean maps. The strength of BMS is verified by its simplicity, efficiency and superior performance compared with 10 state-of-the-art methods on seven eye tracking benchmark datasets.

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

[2]  Petros Maragos,et al.  Threshold Superposition in Morphological Image Analysis Systems , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

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

[4]  John K. Tsotsos,et al.  Saliency, attention, and visual search: an information theoretic approach. , 2009, Journal of vision.

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

[6]  Jian Sun,et al.  Geodesic Saliency Using Background Priors , 2012, ECCV.

[7]  Jorge Stolfi,et al.  The image foresting transform: theory, algorithms, and applications , 2004 .

[8]  Ruth Kimchi,et al.  Figure-Ground Segmentation Can Occur Without Attention , 2008, Psychological science.

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

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

[11]  Nuno Vasconcelos,et al.  Saliency-based discriminant tracking , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

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

[13]  Ali Borji,et al.  Analysis of Scores, Datasets, and Models in Visual Saliency Prediction , 2013, 2013 IEEE International Conference on Computer Vision.

[14]  Esa Rahtu,et al.  Fast and Efficient Saliency Detection Using Sparse Sampling and Kernel Density Estimation , 2011, SCIA.

[15]  L. Devroye.  Non-Uniform Random Variate Generation , 1986 .

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

[17]  Cristian Sminchisescu,et al.  Dynamic Eye Movement Datasets and Learnt Saliency Models for Visual Action Recognition , 2012, ECCV.

[18]  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.

[19]  Jessika Weiss,et al.  Vision Science Photons To Phenomenology , 2016 .

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

[21]  L Chen,et al.  Topological structure in visual perception. , 1982, Science.

[22]  Mudar Sarem,et al.  Saliency modeling via outlier detection , 2014, J. Electronic Imaging.

[23]  Luc Vincent,et al.  Morphological grayscale reconstruction in image analysis: applications and efficient algorithms , 1993, IEEE Trans. Image Process..

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

[25]  Christof Koch,et al.  Predicting human gaze using low-level saliency combined with face detection , 2007, NIPS.

[26]  Shijian Lu,et al.  Robust and Efficient Saliency Modeling from Image Co-Occurrence Histograms , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[28]  Lihi Zelnik-Manor,et al.  Context-aware saliency detection , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[29]  Yoichi Sato,et al.  Appearance-Based Gaze Estimation Using Visual Saliency , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[31]  G. Baylis,et al.  Shape-coding in IT cells generalizes over contrast and mirror reversal, but not figure-ground reversal , 2001, Nature Neuroscience.

[32]  Pietro Perona,et al.  Is bottom-up attention useful for object recognition? , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[33]  O. Reiser,et al.  Principles Of Gestalt Psychology , 1936 .

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

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

[36]  R. Rosenholtz.  Search asymmetries? What search asymmetries? , 2001, Perception & psychophysics.

[37]  Frédo Durand,et al.  A Benchmark of Computational Models of Saliency to Predict Human Fixations , 2012 .

[38]  Z Kourtzi,et al.  Representation of Perceived Object Shape by the Human Lateral Occipital Complex , 2001, Science.

[39]  Carlo Umiltà,et al.  Foreground–background segmentation and attention: A change blindness study , 2005, Psychological research.

[40]  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.

[41]  Stephen Wallace,et al.  Figure and Ground , 1982 .

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

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

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

[45]  Aykut Erdem,et al.  Visual saliency estimation by nonlinearly integrating features using region covariances. , 2013, Journal of vision.

[46]  Punam K. Saha,et al.  The minimum barrier distance , 2013, Comput. Vis. Image Underst..

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

[48]  Deepu Rajan,et al.  Random walks on graphs to model saliency in images , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[49]  Punam K. Saha,et al.  Efficient algorithm for finding the exact minimum barrier distance , 2014, Comput. Vis. Image Underst..

[50]  J. Wolfe,et al.  What attributes guide the deployment of visual attention and how do they do it? , 2004, Nature Reviews Neuroscience.

[51]  Iain D. Gilchrist,et al.  Visual correlates of fixation selection: effects of scale and time , 2005, Vision Research.

[52]  Rainer Stiefelhagen,et al.  Quaternion-Based Spectral Saliency Detection for Eye Fixation Prediction , 2012, ECCV.

[53]  Stan Sclaroff,et al.  Saliency Detection: A Boolean Map Approach , 2013, 2013 IEEE International Conference on Computer Vision.

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

[55]  Harold Pashler,et al.  A Boolean map theory of visual attention. , 2007, Psychological review.

[56]  Yifan Peng,et al.  Studying Relationships between Human Gaze, Description, and Computer Vision , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[57]  Punam K. Saha,et al.  Fuzzy Distance Transform: Theory, Algorithms, and Applications , 2002, Comput. Vis. Image Underst..

[58]  Matthias Bethge,et al.  Deep Gaze I: Boosting Saliency Prediction with Feature Maps Trained on ImageNet , 2014, ICLR.

[59]  Jorge Stolfi,et al.  The image foresting transform: theory, algorithms, and applications , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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