Morphological PDEs on graphs for saliency detection

Visual saliency is a computational process that seeks to identify the most attention drawing regions from a visual point. In this study, the authors propose a new algorithm to estimate the saliency based on partial difference equations (PDEs) method. A local or non-local graph is first constructed from the geometry of images. Then, the transcription of PDE on graph is done and resolved by using the mean curvature flow that can be used to perform regularisation and the Eikonal equation for segmentation. Finally, an extended region adjacency graph is built, which is extended with a k-nearest neighbour graph, in the mean RGB colour space of each region in order to estimate saliency. The proposed algorithm allows to unify a local or non-local graph processing for saliency computing. Furthermore, it works on discrete data of arbitrary topology. For evaluation, the proposed method is tested on two different datasets and 3D point clouds. Extensive experimental results show the applicability and effectiveness of the proposed algorithm.

[1]  Lu Li,et al.  Saliency detection based on foreground appearance and background-prior , 2018, Neurocomputing.

[2]  Abderrahim Elmoataz,et al.  Mean curvature flow on graphs for image and manifold restoration and enhancement , 2014, Signal Process..

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

[4]  LinLin Shen,et al.  Visual-Patch-Attention-Aware Saliency Detection , 2015, IEEE Transactions on Cybernetics.

[5]  Kin-Man Lam,et al.  Facial-feature detection and localization based on a hierarchical scheme , 2014, Inf. Sci..

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

[7]  Jingdong Wang,et al.  Salient Object Detection: A Discriminative Regional Feature Integration Approach , 2013, International Journal of Computer Vision.

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

[9]  Neil A. Dodgson,et al.  Cluster-Based Point Set Saliency , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[10]  Bu-Sung Lee,et al.  Bottom-Up Saliency Detection Model Based on Human Visual Sensitivity and Amplitude Spectrum , 2012, IEEE Transactions on Multimedia.

[11]  Abderrahim Elmoataz,et al.  Nonlocal Discrete Regularization on Weighted Graphs: A Framework for Image and Manifold Processing , 2008, IEEE Transactions on Image Processing.

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

[13]  James H. Elder,et al.  Design and perceptual validation of performance measures for salient object segmentation , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops.

[14]  Chun Qi,et al.  Saliency detection based on global and local short-term sparse representation , 2016, Neurocomputing.

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

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

[17]  Jie Yang,et al.  Saliency Detection by Fully Learning a Continuous Conditional Random Field , 2017, IEEE Transactions on Multimedia.

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

[19]  Zhi Liu,et al.  Nonparametric saliency detection using kernel density estimation , 2010, 2010 IEEE International Conference on Image Processing.

[20]  Huchuan Lu,et al.  Bayesian Saliency via Low and mid Level Cues , 2022 .

[21]  Yizhou Yu,et al.  Deep Contrast Learning for Salient Object Detection , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[22]  Shang-Hong Lai,et al.  Fusing generic objectness and visual saliency for salient object detection , 2011, 2011 International Conference on Computer Vision.

[23]  Abderrahim Elmoataz,et al.  Partial Difference Operators on Weighted Graphs for Image Processing on Surfaces and Point Clouds , 2014, IEEE Transactions on Image Processing.

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

[25]  Abderrahim Elmoataz,et al.  Eikonal Equation Adaptation on Weighted Graphs: Fast Geometric Diffusion Process for Local and Non-local Image and Data Processing , 2012, Journal of Mathematical Imaging and Vision.

[26]  Stéphane Lafon,et al.  Diffusion maps , 2006 .

[27]  Li Xu,et al.  Hierarchical Saliency Detection , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[28]  Yizhou Yu,et al.  Visual saliency based on multiscale deep features , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[29]  Yu Guo,et al.  Point-wise saliency detection on 3D point clouds via covariance descriptors , 2017, The Visual Computer.

[30]  Abderrahim Elmoataz,et al.  Nonlocal PDEs on Graphs: From Tug-of-War Games to Unified Interpolation on Images and Point Clouds , 2017, Journal of Mathematical Imaging and Vision.

[31]  Huchuan Lu,et al.  Saliency detection via background and foreground seed selection , 2015, Neurocomputing.

[32]  Hyunjun Eun,et al.  Saliency refinement: Towards a uniformly highlighted salient object , 2018, Signal Process. Image Commun..