logo

SCIENTIA SINICA Informationis, Volume 51 , Issue 9 : 1475(2021) https://doi.org/10.1360/SSI-2020-0370

Cognitive vision inspired object segmentation metric and loss function

More info
  • ReceivedNov 30, 2020
  • AcceptedFeb 22, 2021
  • PublishedSep 17, 2021

Abstract


Funded by

新一代人工智能重大项目(2018AAA0100400)

国家自然科学基金优秀青年科学基金项目(61922046)

教育部指导高校科技创新规划项目和南开大学中央高校基本科研业务费专项资金项目(63201169)


References

[1] Qin C, Zhang G, Zhou Y. Integration of the saliency-based seed extraction and random walks for image segmentation. Neurocomputing, 2014, 129: 378-391 CrossRef Google Scholar

[2] Li G, Yu Y. Visual saliency based on multiscale deep features. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015. 5455--5463. Google Scholar

[3] Zhang D, Meng D, Han J. Co-Saliency Detection via a Self-Paced Multiple-Instance Learning Framework.. IEEE Trans Pattern Anal Mach Intell, 2017, 39: 865-878 CrossRef PubMed Google Scholar

[4] Zhang P, Wang D, Lu H, et al. Amulet: Aggregating multi-level convolutional features for salient object detection. In: Proceedings of IEEE International Conference on Computer Vision (ICCV), 2017. 202--211. Google Scholar

[5] Li C, Cong R, Piao Y, et al. RGB-D salient object detection with cross-modality modulation and selection. In: Proceedings of European Conference on Computer Vision, 2020. 225--241. Google Scholar

[6] Zhang D, Tian H, Han J. Few-cost salient object detection with adversarial-paced learning. Adv. Neural Inform. Process. Syst. 2020, 33. Google Scholar

[7] Fan D P, Lin Z, Zhang Z. Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks.. IEEE Trans Neural Netw Learning Syst, 2020, : 1-1 CrossRef PubMed Google Scholar

[8] Fan D P, Li T, Lin Z. Re-thinking Co-Salient Object Detection.. IEEE Trans Pattern Anal Mach Intell, 2021, : 1-1 CrossRef PubMed Google Scholar

[9] Tian Y, Li J, Yu S. Learning Complementary Saliency Priors for Foreground Object Segmentation in Complex Scenes. Int J Comput Vis, 2015, 111: 153-170 CrossRef Google Scholar

[10] Chen C, Li S, Wang Y. Video Saliency Detection via Spatial-Temporal Fusion and Low-Rank Coherency Diffusion. IEEE Trans Image Process, 2017, 26: 3156-3170 CrossRef PubMed ADS Google Scholar

[11] Kanan C, Cottrell G. Robust classification of objects, faces, and flowers using natural image statistics. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010. 2472--2479. Google Scholar

[12] Rutishauser U, Walther D, Koch C, et al. Is bottom-up attention useful for object recognition? In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. Google Scholar

[13] Arbeláez P, Maire M, Fowlkes C. Contour detection and hierarchical image segmentation.. IEEE Trans Pattern Anal Mach Intell, 2011, 33: 898-916 CrossRef PubMed Google Scholar

[14] Jaccard P. Étude comparative de la distribution florale dans une portion des alpes et des jura. Bull Soc Vaudoise Sci Nat, 1901, 37:547--579 doi: 10.5169/seals-266450. Google Scholar

[15] Everingham M, Van Gool L, Williams C K I. The Pascal Visual Object Classes (VOC) Challenge. Int J Comput Vis, 2010, 88: 303-338 CrossRef Google Scholar

[16] Csurka G, Larlus D, Perronnin F, et al. What is a good evaluation measure for semantic segmentation? In: Proceedings of British Machine Vision Conference, 2013. Google Scholar

[17] Margolin R, Zelnik-Manor L, Tal A. How to evaluate foreground maps? In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2014. 248--255. Google Scholar

[18] Shi R, Ngan K N, Li S. Visual Quality Evaluation of Image Object Segmentation: Subjective Assessment and Objective Measure. IEEE Trans Image Process, 2015, 24: 5033-5045 CrossRef PubMed ADS Google Scholar

[19] Movahedi V, Elder J H. Design and perceptual validation of performance measures for salient object segmentation. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010. 49--56. Google Scholar

[20] Villegas P, Marichal X. Perceptually-Weighted Evaluation Criteria for Segmentation Masks in Video Sequences. IEEE Trans Image Process, 2004, 13: 1092-1103 CrossRef PubMed ADS Google Scholar

[21] McGuinness K, O'Connor N E. A comparative evaluation of interactive segmentation algorithms. Pattern Recognition, 2010, 43: 434-444 CrossRef Google Scholar

[22] Fan D P, Gong C, Cao Y, et al. Enhanced-alignment measure for binary foreground map evaluation. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence, 2018. 698--704. Google Scholar

[23] Li G, Yu Y. Deep contrast learning for salient object detection. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016. 478--487. Google Scholar

[24] Wang L, Wang L, Lu H, et al. Saliency detection with recurrent fully convolutional networks. In: Proceedings of European Conference on Computer Vision, 2016. 825--841. Google Scholar

[25] Liu N, Han J. DHSNet: deep hierarchical saliency network for salient object detection. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016. 678--686. Google Scholar

[26] Cheng M M, Mitra N J, Huang X. Global Contrast Based Salient Region Detection.. IEEE Trans Pattern Anal Mach Intell, 2015, 37: 569-582 CrossRef PubMed Google Scholar

[27] Tie Liu , Zejian Yuan , Jian Sun . Learning to detect a salient object.. IEEE Trans Pattern Anal Mach Intell, 2011, 33: 353-367 CrossRef PubMed Google Scholar

[28] Wang Z, Bovik A C, Sheikh H R. Image Quality Assessment: From Error Visibility to Structural Similarity. IEEE Trans Image Process, 2004, 13: 600-612 CrossRef PubMed ADS Google Scholar

[29] Pont-Tuset J, Marques F. Measures and meta-measures for the supervised evaluation of image segmentation. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2013. 2131--2138. Google Scholar

[30] Fan D P, Cheng M M, Liu Y, et al. Structure-measure: a new way to evaluate foreground maps. In: Proceedings of IEEE International Conference on Computer Vision, 2017. 4548--4557. Google Scholar

[31] Li Y, Hou X, Koch C, et al. The secrets of salient object segmentation. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2014. 280--287. Google Scholar

[32] Yulin Xie , Huchuan Lu , Ming-Hsuan Yang . Bayesian Saliency via Low and Mid Level Cues. IEEE Trans Image Process, 2013, 22: 1689-1698 CrossRef PubMed ADS Google Scholar

[33] Martin D, Fowlkes C, Tal D, et al. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings of IEEE International Conference on Computer Vision, 2001. 416--423. Google Scholar

[34] Zhi Liu , Wenbin Zou , Le Meur O. Saliency Tree: A Novel Saliency Detection Framework. IEEE Trans Image Process, 2014, 23: 1937-1952 CrossRef PubMed ADS Google Scholar

[35] Wang J, Jiang H, Yuan Z. Salient Object Detection: A Discriminative Regional Feature Integration Approach. Int J Comput Vis, 2017, 123: 251-268 CrossRef Google Scholar

[36] Li X, Lu H, Zhang L, et al. Saliency detection via dense and sparse reconstruction. In: Proceedings of IEEE International Conference on Computer Vision, 2013. 2976--2983. Google Scholar

[37] Zhao R, Ouyang W, Li H, et al. Saliency detection by multi-context deep learning. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2015. 1265--1274. Google Scholar

[38] Chen T, Lin L, Liu L. DISC: Deep Image Saliency Computing via Progressive Representation Learning.. IEEE Trans Neural Netw Learning Syst, 2016, 27: 1135-1149 CrossRef PubMed Google Scholar

[39] Lee G, Tai Y W, Kim J. Deep saliency with encoded low level distance map and high level features. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016. 660--668. Google Scholar

[40] Best D and Roberts D. Algorithm as 89: the upper tail probabilities of spearman's rho. J. R. Stat. Soc. Ser. C. App. Stat., 1975, 24(3):377--379. Google Scholar

[41] Wei J, Wang S, Huang Q. F$^3$net: fusion, feedback and focus for salient object detection. In: Proceedings of the Association for the Advance of Artificial Intelligence, 2020. 12321--12328. Google Scholar

[42] Achanta R, Hemami S, Estrada F, et al. Frequency-tuned salient region detection. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2009. 1597--1604. Google Scholar

[43] Perazzi F, Krähenbühl P, Pritch Y, et al. Saliency filters: contrast based filtering for salient region detection. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2012. 733--740. Google Scholar

[44] Wu Z, Su L, Huang Q. Stacked cross refinement network for edge-aware salient object detection. In: Proceedings of IEEE International Conference on Computer Vision, 2019. 7264--7273. Google Scholar

[45] Wang L, Lu H, Wang Y, et al. Learning to detect salient objects with image-level supervision. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2017. 136--145. Google Scholar

[46] Yang C, Zhang L, Lu H, et al. Saliency detection via graph-based manifold ranking. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2013. 3166--3173. Google Scholar

[47] Li G, Yu Y. Visual Saliency Detection Based on Multiscale Deep CNN Features. IEEE Trans Image Process, 2016, 25: 5012-5024 CrossRef PubMed ADS arXiv Google Scholar

[48] Fan D P, Ji G P, Sun G, et al. Camouflaged object detection. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2020. 2777--2787. Google Scholar

[49] Le T N, Nguyen T V, Nie Z. Anabranch network for camouflaged object segmentation. Comput Vision Image Understanding, 2019, 184: 45-56 CrossRef Google Scholar

[50] Skurowski P, Abdulameer H, Błaszczyk J, et al. Animal camouflage analysis: Chameleon database. Unpublished Manuscript, 2018. Google Scholar

[51] Fan D P, Ji G P, Zhou T, et al. Pranet: parallel reverse attention network for polyp segmentation. In: Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention, 2020. 263--273. Google Scholar

[52] Jha D, Smedsrud P H, Riegler M A, et al. Kvasir-seg: a segmented polyp dataset. In: Proceedings of International Conference Multimedia Modeling, 2020. 451--462. Google Scholar

[53] Bernal J, Sánchez F J, Fernández-Esparrach G. WM-DOVA maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians.. Computized Med Imag Graphics, 2015, 43: 99-111 CrossRef PubMed Google Scholar

[54] Vázquez D, Bernal J, Sánchez F J. A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images.. J Healthcare Eng, 2017, 2017(1): 1-9 CrossRef PubMed Google Scholar

[55] Zhang D, Huang G, Zhang Q. Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition, 2021, 110: 107562 CrossRef Google Scholar

[56] Zhang D, Huang G, Zhang Q. Exploring Task Structure for Brain Tumor Segmentation From Multi-Modality MR Images. IEEE Trans Image Process, 2020, 29: 9032-9043 CrossRef PubMed ADS Google Scholar

[57] Zhang D, Zhang J, Zhang Q. Automatic pancreas segmentation based on lightweight DCNN modules and spatial prior propagation. Pattern Recognition, 2021, 114: 107762 CrossRef Google Scholar

qqqq

Contact and support