Multiple feature distinctions based saliency flow model

Pattern Recognition - Tập 54 - Trang 190-205 - 2016
Xiujun Zhang1,2, Xiaoli Sun3, Chen Xu4, George Baciu2
1College of Information Engineering, Shenzhen University, Shenzhen 518060, China
2GAMA Lab, Department of Computing, The Hong Kong Polytechnic University, Hong Kong
3College of Mathematics and Computational Science, Shenzhen University, Shenzhen 518060, China
4Institute of Intelligent Computing Science, Shenzhen University, Shenzhen 518060, China

Tài liệu tham khảo

U. Rutishauser, D. Walther, C. Koch, P. Perona, Is bottom-up attention useful for object recognition? in: Proceedings of IEEE Conference on Computer Vision Pattern Recognition (CVPR), IEEE, 2004, pp. 37–44. Itti, 2004, Automatic foveation for video compression using a neurobiological model of visual attention, IEEE Trans. Image Process., 13, 1304, 10.1109/TIP.2004.834657 Hou, 2013, Visual saliency detection using information divergence, Pattern Recognit., 46, 2658, 10.1016/j.patcog.2013.03.008 Yu, 2010, An object-based visual attention model for robotic applications, IEEE Trans. Syst. Man Cybern. B Cybern., 40, 1398, 10.1109/TSMCB.2009.2038895 C. Li, G. Baciu, Y. Wang, Modulgraph: modularity-based visualization of massive graphs, in: SIGGRAPH Asia 2015 Visualization in High Performance Computing, ACM, Kobe, Japan, 2015, pp. 11:1–11:4. Itti, 1998, A model of saliency-based visual attention for rapid scene analysis, IEEE Trans. Pattern Anal. Mach. Intell., 20, 1254, 10.1109/34.730558 Bruce, 2005, Features that draw visual attention, Neurocomputing, 65, 125, 10.1016/j.neucom.2004.10.065 Harel, 2007, Graph-based visual saliency, Adv. Neural, 19, 545 X. Hou, L. Zhang, Saliency detection: a spectral residual approach, in: Proceedings of IEEE Conference on Computer Vision Pattern Recognition (CVPR), IEEE, 2007, pp. 1–8. Momtaz, 2015, Differences of eye movement pattern in natural and man-made scenes and image categorization with the help of these patterns, J. Integr. Neurosci., 14, 1650002 Zanganeh Momtaz, 2015, Predicting the eye fixation locations in the gray scale images in the visual scenes with different semantic contents, Cognit. Neurodyn., 10, 1 C. Guo, Q. Ma, L. Zhang, Spatio-temporal saliency detection using phase spectrum of quaternion Fourier transform, in: Proceedings of IEEE Conference on Computer Vision Pattern Recognition (CVPR), IEEE, 2008, pp. 1–8. Borji, 2013, State-of-the-art in visual attention modeling, IEEE Trans. Pattern Anal. Mach. Intell., 35, 185, 10.1109/TPAMI.2012.89 A. Borji, D.N. Sihite, L. Itti, Salient object detection: a benchmark, in: Proceedings of European Conference on Computer Vision (ECCV), 2012, pp. 414–429. Q. Yan, L. Xu, J. Shi, J. Jia, Hierarchical saliency detection, in: Proceedings of IEEE Conference on Computer Vision Pattern Recognition (CVPR), IEEE, 2013, pp. 1155–1162. C. Yang, L. Zhang, H. Lu, X. Ruan, M.-H. Yang, Saliency detection via graph-based manifold ranking, in: Proceedings of IEEE Conference on Computer Vision Pattern Recognition (CVPR), IEEE, 2013, pp. 3166–3173. Wenbin, 2013, Segmentation driven low-rank matrix recovery for saliency detection, Proc. BMVC, 1 Y. Li, X. Hou, C. Koch, J. Rehg, A. Yuille, The secrets of salient object segmentation, in: Proceedings of IEEE Conference on Computer Vision Pattern Recognition (CVPR), IEEE, 2014, pp. 280–287. X. Shen, Y. Wu, A unified approach to salient object detection via low rank matrix recovery, in: Proceedings of IEEE Conference on Computer Vision Pattern Recognition (CVPR), IEEE, 2012, pp. 853–860. H. Jiang, J. Wang, Z. Yuan, Y. Wu, N. Zheng, S. Li, Salient object detection: a discriminative regional feature integration approach, in: Proceedings of IEEE Conference on Computer Vision Pattern Recognition (CVPR), IEEE, 2013, pp. 2083–2090. Treisman, 1980, A feature-integration theory of attention, Cognit. Psychol., 12, 97, 10.1016/0010-0285(80)90005-5 M.-M. Cheng, G.-X. Zhang, N.J. Mitra, X. Huang, S.-M. Hu, Global contrast based salient region detection, in: Proceedings of IEEE Conference on Computer Vision Pattern Recognition (CVPR), IEEE, 2011, pp. 409–416. Goferman, 2012, Context-aware saliency detection, IEEE Trans. Pattern Anal. Mach. Intell., 34, 1915, 10.1109/TPAMI.2011.272 K.-Y. Chang, T.-L. Liu, H.-T. Chen, S.-H. Lai, Fusing generic objectiveness and visual saliency for salient object detection, in: Proceedings of IEEE International Conference on Computer Vision (ICCV), IEEE, 2011, pp. 914–921. Jiang, 2011, Automatic salient object segmentation based on context and shape prior, Proc. BMVC, 7 R. Margolin, A. Tal, L. Zelnik-Manor, What makes a patch distinct? in: Proceedings of IEEE Conference on Computer Vision Pattern Recognition (CVPR), IEEE, 2013, pp. 1139–1146. R. Achanta, S. Hemami, F. Estrada, S. Susstrunk, Frequency-tuned salient region detection, in: Proceedings of IEEE Conference on Computer Vision Pattern Recognition (CVPR), IEEE, 2009, pp. 1597–1604. W. Zhu, S. Liang, Y. Wei, J. Sun, Saliency optimization from robust background detection, in: Proceedings of IEEE Conference on Computer Vision Pattern Recognition (CVPR), IEEE, 2014, pp. 2814–2821. Zhang, 2015, Study of visual saliency detection via nonlocal anisotropic diffusion equation, Pattern Recognit., 48, 1315, 10.1016/j.patcog.2014.10.016 F. Perazzi, P. Krahenbuhl, Y. Pritch, A. Hornung, Saliency filters: contrast based filtering for salient region detection, in: Proceedings of IEEE Conference on Computer Vision Pattern Recognition (CVPR), IEEE, 2012, pp. 733–740. R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, S. Susstrunk, Slic superpixels, EPFL, Technical Report, vol. 2, 2010, p. 3. Liu, 2011, Learning to detect a salient object, IEEE Trans. Pattern Anal. Mach. Intell., 33, 353, 10.1109/TPAMI.2010.70 X.R. Na Tong, Huchuan Lu, M.-H. Yang, Salient object detection via bootstrap learning, in: Proceedings of IEEE Conference on Computer Vision Pattern Recognition (CVPR), IEEE, 2015, pp. 1884–1892. Yuan, 2014, A spatially continuous max-flow and min-cut framework for binary labeling problems, Numerische Mathematik, 126, 559, 10.1007/s00211-013-0569-x