视觉和物体显著性检测方法
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摘要
显著性检测的目标是快速找出图像视频等视觉数据中最吸引人注意的区域,作为计算机视觉领域的基本任务之一,近年来备受关注,众多的方法被提出。这些显著性检测工作可分为2个分支:视觉显著性检测方法和显著性物体检测方法。尽管这2个分支的方法有很多相同点甚至共享相同的计算模型,但是在不同分支的评价数据集上有巨大的性能差异,很少有工作对这2个分支的方法进行比较和分析。通过详细分析和阐述2个分支主流方法的计算模型、采用的评价机制以及使用的数据集,总结了多种改进视觉显著性检测方法用来检测显著性物体的方式,通过这些方式视觉显著性检测方法可应用于显著性物体检测数据集,其性能达到了领先水平甚至超过了一些主流显著性物体检测方法,从而缓解了2个分支显著性检测方法在不同分支数据集上表现的不一致的问题。
Saliency detection aims to locate the most attractive areas in image or video data, as the basic task of computer vision field, has receive intensive attentions. Many methods have been proposed recently, these methods usually can be classified into two branches: visual saliency detection and salient object detection. Tough the methods of two branches usually share the similar features and even the frameworks, their performances on datasets of different branch have large gap, seldom works have compared and analyzed them. In this work, we will provide a detailed review and analysis of main works in two branches, including their mechanism, metrics and datasets. Besides, in this work, we summarized approaches to boost visual saliency detection methods for the task of salient object detection. With these approaches, visual saliency detection methods can be applied to detect the salient object and show superior performance that even outperform some specialized state-of-the-art salient object detection methods, thus reduce the performance inconsistence in different specialized datasets.
Saliency detection aims to locate the most attractive areas in image or video data, as the basic task of computer vision field, has receive intensive attentions. Many methods have been proposed recently, these methods usually can be classified into two branches: visual saliency detection and salient object detection. Tough the methods of two branches usually share the similar features and even the frameworks, their performances on datasets of different branch have large gap, seldom works have compared and analyzed them. In this work, we will provide a detailed review and analysis of main works in two branches, including their mechanism, metrics and datasets. Besides, in this work, we summarized approaches to boost visual saliency detection methods for the task of salient object detection. With these approaches, visual saliency detection methods can be applied to detect the salient object and show superior performance that even outperform some specialized state-of-the-art salient object detection methods, thus reduce the performance inconsistence in different specialized datasets.
引文
[1] BRUCE N, TSOTSOS J. Saliency based on information maximization[C]// Proceedings of 18th Advances in Neural Information Processing Systems (NIPS 2005). Vancouver: MIT Press, 2005: 155-162.
[2] BRUCE N, TSOTSOS J. Saliency, attention, and visual search: an information theoretic approach[J]. Journal of Vision, 2009, 3(9):5.1-24.
[3] FRINTROP S, WERNER T, GARCIA M. Traditional saliency reloaded: a good old model in new shape[C]// Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2015). Boston: MA, 2015: 82-90.
[4] GAO D, VASCONCELOS N. Decision-theoretic saliency: computational principles, biological plausibility, and implications for neurophysiology and psychophysics[J]. Neural Computation, 2009, 21(1): 239-271.
[5] HAREL J, KOCH C, PERONA P. Graph-based visual saliency[C]// Proceedings of 19th Advances in Neural Information Processing Systems (NIPS 2006). Canada: MIT Press, 2006: 545-552.
[6] HOU X, ZHANG L. Saliency detection: a spectral residual approach[C]// Proceedings of 2007 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2007). Minneapolis: MN, 2007: 1-8.
[7] ITTI L, KOCH C, NIEBUR E. A model of saliency-based visual attention for rapid scene analysis[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(11): 1254-1259.
[8] JUDD T, EHINGER K, DURAND F, et al. Learning to predict where humans look[C]// Proceedings of 2009 International Conference on Computer Vision (ICCV 2009). Kyoto: IEEE, 2009: 2106-2113.
[9] KLEIN D, FRINTROP S. Center-surround divergence of feature statistics for salient object detection[C]// Proceedings of 2011 International Conference on Computer Vision (ICCV 2011). Barcelona: IEEE, 2011: 2214-2219.
[10] SEO H, MILANFAR P. Static and space-time visual saliency detection by self-resemblance[J]. Journal of Vision, 2009, 9(12): 15.1-27.
[11] YANG J, YANG M. Top-down visual saliency via joint crf and dictionary learning[C]// Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2012). Rhode Island: IEEE, 2012: 2296-2303.
[12] ZHANG L, TONG M H, MARKS T K, et al. Sun: a Bayesian framework for saliency using natural statistics[J]. Journal of Vision, 2008, 8(7): 32.1-20.
[13] ACHANTA R, ESTRADA F, WILS P, et al. Salient region detection and segmentation[C]// Proceedings of 2008 International Conference on Computer Vision Systems (ICVS 2008). Berlin: Springer, 2008: 66-75.
[14] ACHANTA R, HEMAMI S, ESTRADA F, et al. Frequency-tuned salient region detection[C]// Proceedings of 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2009). Miami: IEEE, 2009: 1597-1604.
[15] CHANG K, LIU T, CHEN H, et al. Fusing generic objectness and visual saliency for salient object detection[C]// Proceedings of 2011 International Conference on Computer Vision (ICCV 2011). Barcelona: IEEE, 2011: 914-921.
[16] CHENG M, MITRA N J, HUANG X, et al. Global contrast based salient region detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(3): 569-582.
[17] CHENG M, WARRELL J, LIN W, et al. Efficient salient region detection with soft image abstraction[C]// Proceedings of 2013 International Conference on Computer Vision (ICCV 2013). Sydney: IEEE, 2013: 1529-1536.
[18] FELZENSZWALB P F, HUTTENLOCHER D P. Efficient graph-based image segmentation[J]. International Journal of Computer Vision, 2004, 59(2): 167-181.
[19] GOFERMAN S, ZELNIK-MANOR L, TAL A. Context-aware saliency detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(10): 1915-1926.
[20] JIANG B, ZHANG L, LU H, et al. Saliency detection via absorbing markov chain[C]// Proceedings of 2013 International Conference on Computer Vision (ICCV 2013). Sydney: IEEE, 2013: 1665-1672.
[21] JIANG H, WANG J, YUAN Z, et al. Salient object detection: a discriminative regional feature integration approach[C]// Proceedings of 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2013). Portland: IEEE, 2013: 2083-2090.
[22] JIANG P, LING H, YU J,et al. Salient region detection by ufo: uniqueness, focusness and objectness[C]// Proceedings of 2013 International Conference on Computer Vision (ICCV 2013). Syndney: IEEE, 2013: 1976-1983.
[23] KIM J, HAN D, TAI Y W, et al. Salient region detection via high-dimensional color transform[C]// Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2014). Columbus: IEEE, 2014: 883-890.
[24] LI X, LU H, ZHANG L,et al. Saliency detection via dense and sparse reconstruction[C]// Proceedings of 2013 International Conference on Computer Vision (ICCV 2013). Sydney: IEEE, 2013: 2976-2983.
[25] LIU R, CAO J, LIN Z, et al. Adaptive partial differential equation learning for visual saliency detection[C]// Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2014). Columbus: IEEE, 2014: 3866-3873.
[26] LIU T, YUAN Z, SUN J, et al. Learning to detect a salient object[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(2): 353-367.
[27] LU S, MAHADEVAN V, VASCONCELOS N. Learning optimal seeds for diffusion-based salient object detection[C]// Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2014). Columbus: IEEE, 2014: 2790-2797.
[28] MAI L, NIU Y, LIU F. Saliency aggregation: a data-driven approach[C]// Proceedings of 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2013). Portland: IEEE, 2013: 1131-1138.
[29] MARGOLIN R, TAL A, ZELNIK-MANOR L. What makes a patch distinct?[C]// Proceedings of 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2013). Portland: IEEE, 2013: 1139-1146.
[30] PERAZZI F, KRAHENBUHL P, PRITCH Y, et al. Saliency filters: contrast based filtering for salient region detection[C]// Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2012). Providence: IEEE, 2012: 733-740.
[31] QIN Y, LU H, XU Y, et al. Saliency detection via cellular automata[C]// Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2015). Boston: IEEE, 2015: 110-119.
[32] REN Z, HU Y, CHIA L T, et al. Improved saliency detection based on superpixel clustering and saliency propagation[C]// Proceedings of 18th ACM international conference on Multimedia (MM2010). Firenze: ACM, 2010: 1099-1102.
[33] SHEN X, WU Y. A unified approach to salient object detection via low rank matrix recovery[C]// Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2012). Providence: IEEE, 2012: 853-860.
[34] SIVA P, RUSSELL C, XIANG T, et al. Looking beyond the image: unsupervised learning for object saliency and detection[C]// Procceedings of 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2013). Portland: IEEE, 2013: 3238-3245.
[35] XIE Y, LU H, YANG M. Bayesian saliency via low and mid level cues[J]. IEEE Transactions on Image Processing, 2013, 22(5): 1689-1698.
[36] YAN Q, XU L, SHI J, et al. Hierarchical saliency detection[C]// Proceedings of 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2013). Portland: IEEE, 2013: 1155-1162.
[37] YANG C, ZHANG L, LU H, et al. Saliency detection via graph-based manifold ranking[C]// Proceedings of 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2013). Portland: IEEE, 2013: 3166-3173.
[38] ZHU W, LIANG S, WEI Y, et al. Saliency optimization from robust background detection[C]// Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2014). Columbus: IEEE, 2014: 2814-2821.
[39] ZOU W, KOMODAKIS N. Harf: hierarchy-associated rich features for salient object detection[C]// Proceedings of 2015 International Conference on Computer Vision (ICCV 2015). Santiago: IEEE, 2015: 406-414.
[40] 王军,吴泽民,杨巍,等.基于稀疏恢复与优化的显著性目标检测算法[J].计算机科学,2018,45(8):258-263.WANG Jun, WU Zemin, YANG Wei, et al. Salient object detection algorithm based on sparse recovery and optimization[J]. Computer Science, 2018, 45(8): 258-263.
[41] 孙赫赫,尚晓清,王冲.基于区域对比的图像显著性检测方法[J].计算机工程与应用,2018,54(10):192-195.SUN Hehe, SHANG Xiaoqing, WANG Chong. Method of saliency detection based on region contrast[J]. Computer Eninerring and Applications, 2018, 54(10): 192-195.
[42] 刘亚宁,吴清,魏雪.基于流行排序的前景背景显著性检测算法[J].科学技术与工程,2018,18(18):74-81.LIU Yaning, WU Qing, WEI Xue. Saliency detection combined foreground with background based on manifold ranking[J]. Science Technology and Engineering, 2018, 18(18): 74-81.
[43] 梁大川,李静,刘赛.基于图和稀疏主成分分析的多目标显著性检测[J].计算机研究与发展,2018,55(5):1078-1089.LIANG Dachuan, LI Jing, LIU Sai. Multiple object saliency detection based on graph and sparse principal component analysis[J]. Journal of Computer Research and Development, 2018, 55(5): 1078-1089.
[44] 侯向丹,刘晓曼,刘洪普,等.基于多尺度多特征的显著性区域检测[J].河北工业大学学报,2017,1(6):26-31.HOU Xiangdan, LIU Xiaoman, LIU Hongpu, et al. Salient region detection via multi-scale and multi-feature[J]. Journal of Hebei University of Technology, 2017, 1(6): 26-31.
[45] ERDEM E, ERDEM A. Visual saliency estimation by non-linearly integrating features using region covariance[J]. Journal of Vision, 2013, 13(4): 11.
[46] JIANG P, VASCONCELOS N, PENG J. Generic promotion of diffusion-based salient object detection[C]// Proceedings of 2015 International Conference on Computer Vision (ICCV 2015). Santiago: IEEE, 2015: 217-225.
[47] ZHANG J, SCLAROFF S. Saliency detection: a boolean map approach[C]// Proceedings of 2013 International Conference on Computer Vision (ICCV 2013). Sydney: IEEE, 2013: 153-160.
[48] ZHANG J, SCLAROFF S, LIN Z, et al. Minimum barrier salient object detection at 80 fps[C]// Proceedings of 2015 International Conference on Computer Vision (ICCV 2015). Santiago: IEEE, 2015: 1404-1412.
[49] 蒋鹏.图像内容显著性检测的理论和方法研究[D].济南:山东大学,2016. JIANG Peng. Research on theories and methodologies for saliency detection from images[D]. Jinan: Shandong Unversity, 2016.
[2] BRUCE N, TSOTSOS J. Saliency, attention, and visual search: an information theoretic approach[J]. Journal of Vision, 2009, 3(9):5.1-24.
[3] FRINTROP S, WERNER T, GARCIA M. Traditional saliency reloaded: a good old model in new shape[C]// Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2015). Boston: MA, 2015: 82-90.
[4] GAO D, VASCONCELOS N. Decision-theoretic saliency: computational principles, biological plausibility, and implications for neurophysiology and psychophysics[J]. Neural Computation, 2009, 21(1): 239-271.
[5] HAREL J, KOCH C, PERONA P. Graph-based visual saliency[C]// Proceedings of 19th Advances in Neural Information Processing Systems (NIPS 2006). Canada: MIT Press, 2006: 545-552.
[6] HOU X, ZHANG L. Saliency detection: a spectral residual approach[C]// Proceedings of 2007 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2007). Minneapolis: MN, 2007: 1-8.
[7] ITTI L, KOCH C, NIEBUR E. A model of saliency-based visual attention for rapid scene analysis[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(11): 1254-1259.
[8] JUDD T, EHINGER K, DURAND F, et al. Learning to predict where humans look[C]// Proceedings of 2009 International Conference on Computer Vision (ICCV 2009). Kyoto: IEEE, 2009: 2106-2113.
[9] KLEIN D, FRINTROP S. Center-surround divergence of feature statistics for salient object detection[C]// Proceedings of 2011 International Conference on Computer Vision (ICCV 2011). Barcelona: IEEE, 2011: 2214-2219.
[10] SEO H, MILANFAR P. Static and space-time visual saliency detection by self-resemblance[J]. Journal of Vision, 2009, 9(12): 15.1-27.
[11] YANG J, YANG M. Top-down visual saliency via joint crf and dictionary learning[C]// Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2012). Rhode Island: IEEE, 2012: 2296-2303.
[12] ZHANG L, TONG M H, MARKS T K, et al. Sun: a Bayesian framework for saliency using natural statistics[J]. Journal of Vision, 2008, 8(7): 32.1-20.
[13] ACHANTA R, ESTRADA F, WILS P, et al. Salient region detection and segmentation[C]// Proceedings of 2008 International Conference on Computer Vision Systems (ICVS 2008). Berlin: Springer, 2008: 66-75.
[14] ACHANTA R, HEMAMI S, ESTRADA F, et al. Frequency-tuned salient region detection[C]// Proceedings of 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2009). Miami: IEEE, 2009: 1597-1604.
[15] CHANG K, LIU T, CHEN H, et al. Fusing generic objectness and visual saliency for salient object detection[C]// Proceedings of 2011 International Conference on Computer Vision (ICCV 2011). Barcelona: IEEE, 2011: 914-921.
[16] CHENG M, MITRA N J, HUANG X, et al. Global contrast based salient region detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(3): 569-582.
[17] CHENG M, WARRELL J, LIN W, et al. Efficient salient region detection with soft image abstraction[C]// Proceedings of 2013 International Conference on Computer Vision (ICCV 2013). Sydney: IEEE, 2013: 1529-1536.
[18] FELZENSZWALB P F, HUTTENLOCHER D P. Efficient graph-based image segmentation[J]. International Journal of Computer Vision, 2004, 59(2): 167-181.
[19] GOFERMAN S, ZELNIK-MANOR L, TAL A. Context-aware saliency detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(10): 1915-1926.
[20] JIANG B, ZHANG L, LU H, et al. Saliency detection via absorbing markov chain[C]// Proceedings of 2013 International Conference on Computer Vision (ICCV 2013). Sydney: IEEE, 2013: 1665-1672.
[21] JIANG H, WANG J, YUAN Z, et al. Salient object detection: a discriminative regional feature integration approach[C]// Proceedings of 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2013). Portland: IEEE, 2013: 2083-2090.
[22] JIANG P, LING H, YU J,et al. Salient region detection by ufo: uniqueness, focusness and objectness[C]// Proceedings of 2013 International Conference on Computer Vision (ICCV 2013). Syndney: IEEE, 2013: 1976-1983.
[23] KIM J, HAN D, TAI Y W, et al. Salient region detection via high-dimensional color transform[C]// Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2014). Columbus: IEEE, 2014: 883-890.
[24] LI X, LU H, ZHANG L,et al. Saliency detection via dense and sparse reconstruction[C]// Proceedings of 2013 International Conference on Computer Vision (ICCV 2013). Sydney: IEEE, 2013: 2976-2983.
[25] LIU R, CAO J, LIN Z, et al. Adaptive partial differential equation learning for visual saliency detection[C]// Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2014). Columbus: IEEE, 2014: 3866-3873.
[26] LIU T, YUAN Z, SUN J, et al. Learning to detect a salient object[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(2): 353-367.
[27] LU S, MAHADEVAN V, VASCONCELOS N. Learning optimal seeds for diffusion-based salient object detection[C]// Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2014). Columbus: IEEE, 2014: 2790-2797.
[28] MAI L, NIU Y, LIU F. Saliency aggregation: a data-driven approach[C]// Proceedings of 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2013). Portland: IEEE, 2013: 1131-1138.
[29] MARGOLIN R, TAL A, ZELNIK-MANOR L. What makes a patch distinct?[C]// Proceedings of 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2013). Portland: IEEE, 2013: 1139-1146.
[30] PERAZZI F, KRAHENBUHL P, PRITCH Y, et al. Saliency filters: contrast based filtering for salient region detection[C]// Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2012). Providence: IEEE, 2012: 733-740.
[31] QIN Y, LU H, XU Y, et al. Saliency detection via cellular automata[C]// Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2015). Boston: IEEE, 2015: 110-119.
[32] REN Z, HU Y, CHIA L T, et al. Improved saliency detection based on superpixel clustering and saliency propagation[C]// Proceedings of 18th ACM international conference on Multimedia (MM2010). Firenze: ACM, 2010: 1099-1102.
[33] SHEN X, WU Y. A unified approach to salient object detection via low rank matrix recovery[C]// Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2012). Providence: IEEE, 2012: 853-860.
[34] SIVA P, RUSSELL C, XIANG T, et al. Looking beyond the image: unsupervised learning for object saliency and detection[C]// Procceedings of 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2013). Portland: IEEE, 2013: 3238-3245.
[35] XIE Y, LU H, YANG M. Bayesian saliency via low and mid level cues[J]. IEEE Transactions on Image Processing, 2013, 22(5): 1689-1698.
[36] YAN Q, XU L, SHI J, et al. Hierarchical saliency detection[C]// Proceedings of 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2013). Portland: IEEE, 2013: 1155-1162.
[37] YANG C, ZHANG L, LU H, et al. Saliency detection via graph-based manifold ranking[C]// Proceedings of 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2013). Portland: IEEE, 2013: 3166-3173.
[38] ZHU W, LIANG S, WEI Y, et al. Saliency optimization from robust background detection[C]// Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2014). Columbus: IEEE, 2014: 2814-2821.
[39] ZOU W, KOMODAKIS N. Harf: hierarchy-associated rich features for salient object detection[C]// Proceedings of 2015 International Conference on Computer Vision (ICCV 2015). Santiago: IEEE, 2015: 406-414.
[40] 王军,吴泽民,杨巍,等.基于稀疏恢复与优化的显著性目标检测算法[J].计算机科学,2018,45(8):258-263.WANG Jun, WU Zemin, YANG Wei, et al. Salient object detection algorithm based on sparse recovery and optimization[J]. Computer Science, 2018, 45(8): 258-263.
[41] 孙赫赫,尚晓清,王冲.基于区域对比的图像显著性检测方法[J].计算机工程与应用,2018,54(10):192-195.SUN Hehe, SHANG Xiaoqing, WANG Chong. Method of saliency detection based on region contrast[J]. Computer Eninerring and Applications, 2018, 54(10): 192-195.
[42] 刘亚宁,吴清,魏雪.基于流行排序的前景背景显著性检测算法[J].科学技术与工程,2018,18(18):74-81.LIU Yaning, WU Qing, WEI Xue. Saliency detection combined foreground with background based on manifold ranking[J]. Science Technology and Engineering, 2018, 18(18): 74-81.
[43] 梁大川,李静,刘赛.基于图和稀疏主成分分析的多目标显著性检测[J].计算机研究与发展,2018,55(5):1078-1089.LIANG Dachuan, LI Jing, LIU Sai. Multiple object saliency detection based on graph and sparse principal component analysis[J]. Journal of Computer Research and Development, 2018, 55(5): 1078-1089.
[44] 侯向丹,刘晓曼,刘洪普,等.基于多尺度多特征的显著性区域检测[J].河北工业大学学报,2017,1(6):26-31.HOU Xiangdan, LIU Xiaoman, LIU Hongpu, et al. Salient region detection via multi-scale and multi-feature[J]. Journal of Hebei University of Technology, 2017, 1(6): 26-31.
[45] ERDEM E, ERDEM A. Visual saliency estimation by non-linearly integrating features using region covariance[J]. Journal of Vision, 2013, 13(4): 11.
[46] JIANG P, VASCONCELOS N, PENG J. Generic promotion of diffusion-based salient object detection[C]// Proceedings of 2015 International Conference on Computer Vision (ICCV 2015). Santiago: IEEE, 2015: 217-225.
[47] ZHANG J, SCLAROFF S. Saliency detection: a boolean map approach[C]// Proceedings of 2013 International Conference on Computer Vision (ICCV 2013). Sydney: IEEE, 2013: 153-160.
[48] ZHANG J, SCLAROFF S, LIN Z, et al. Minimum barrier salient object detection at 80 fps[C]// Proceedings of 2015 International Conference on Computer Vision (ICCV 2015). Santiago: IEEE, 2015: 1404-1412.
[49] 蒋鹏.图像内容显著性检测的理论和方法研究[D].济南:山东大学,2016. JIANG Peng. Research on theories and methodologies for saliency detection from images[D]. Jinan: Shandong Unversity, 2016.