Computer Science ›› 2019, Vol. 46 ›› Issue (9): 36-46.doi: 10.11896/j.issn.1002-137X.2019.09.005
• Surverys • Previous Articles Next Articles
WANG Yan-ran1, CHEN Qing-liang1 , WU Jun-jun2
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[1]GÓMEZ D,YÁÑEZ J,GUADA C,et al.Fuzzy image segmentation based upon hierarchical clustering[J].Knowledge-Based Systems,2015,87(7):26-37. [2]NAZ S,MAJEED H,IRSHAD H.Image segmentation usingfuzzy clustering:A survey[C]//International Conference on Emerging Technologies.Islamabad:IEEE,2010:181-186. [3]PENG B,ZHANG L,ZHANG D.A survey of graph theoretical approaches to image segmentation[J].Pattern Recognition,2013,46(3):1020-1038. [4]LIU S T,YIN F L.The Basic Principle and Its New Advances ofImage Segmentation Methods Based on Graph Cuts[J].Acta Automatica Sinica,2012,38(6):911-922.(in Chinese)刘松涛,殷福亮.基于图割的图像分割方法及其新进展[J].自动化学报,2012,38(6):911-922. [5]YI F,MOON I.Image segmentation:A survey of graph-cutmethods[C]//International Conference on Systems and Informatics.Yantai:IEEE,2012:1936-1941. [6]JIANG F,GU Q,HAO H Z,et al.Survey on Content-Based Image Segmentation Methods[J].Journal of Software,2017,28(1):160-183.(in Chinese)姜枫,顾庆,郝慧珍,等.基于内容的图像分割方法综述[J].软件学报,2017,28(1):160-183. [7]GARCIA-GARCIA A,ORTS-ESCOLANO S,OPREA S,et al.A Review on Deep Learning Techniques Applied to Semantic Segmentation[J].arXiv:1704.06857,2017. [8]SMEULDERS A W M,WORRING M,SANTINI S,et al.Content-Based Image Retrieval at the End of the Early Years[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2000,22(12):1349-1380. [9]DESAI A D,GOLD G E,HARGREAVES B A,et al.Technical Considerations for Semantic Segmentation in MRI using Convolutional Neural Networks[J].arXiv preprint arXiv:1902.01977,2019. [10]MARDIA K V,HAINSWORTH T J.A Spatial ThresholdingMethod for Image Segmentation[J].IEEE transactions on pattern analysis and machine intelligence,1988,10(6):919-927. [11]LAKSHMI S,SANKARANARAYANAN D V.A study of edge detection techniques for segmentation computing approaches[J].International Journal of Computer Applications,2010,CASCT(1):35-41. [12]GIANNAKEAS N,KARVELIS P S,EXARCHOS T P,et al.Segmentation of microarray images using pixel classification-Comparison with clustering-based methods[J].Computers in biology and medicine,2013,43(6):705-716. [13]ADAMS R,BISCHOF L.Seeded region growing[J].IEEETransactions on pattern analysis and machine intelligence,1994,16(6):641-647. [14]LI S Z.Markov random field models in computer vision[C]//European conference on computer vision.Heidelberg:Springer,1994:361-370. [15]LAFFERTY J D,MCCALLUM A,PEREIRA F C N.Condi-tional Random Fields:Probabilistic Models for Segmenting and Labeling Sequence Data[C]//International Conference on Machine Learning.Williamstown:Morgan Kaufmann,2001:282-289. [16]SHI J,MALIK J.Normalized Cuts and Image Segmentation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22(8):888-905. [17]ROTHER C,KOLMOGOROV V,BLAKE A."GrabCut":interactive foreground extraction using iterated graph cuts[J].ACM Transactions on Graphics,2004,23(3):309-314. [18]HENZINGER M,NOE A,SCHULZ C,et al.Practical Minimum Cut Algorithms[J].ACM Journal of Experimental Algorithmics,2018,23(1):1-8. [19]XU H X,TIAN Z,DING M T.Multiscale Segmentation forSAR Image Based on Spectral Clustering and Mixture Model[J].Journal of Image and Graphics,2010,15(3):450-454.(in Chinese)徐海霞,田铮,丁明涛.基于谱聚类与混合模型的SAR图像多尺度分割[J].中国图象图形学报,2010,15(3):450-454. [20]LIU L,SHI Z G,SU H R,et al.Image Segmentation Based on Higher Order Markov Random Field[J].Journal of Computer Research and Development,2013,50(9):1933-1942.(in Chinese)刘磊,石志国,宿浩茹.基于高阶马尔可夫随机场的图像分割[J].计算机研究与发展,2013,50(9):1933-1942. [21]ARBELAEZ P,MAIRE M,FOWLKES C C,et al.Contour Detection and Hierarchical Image Segmentation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2011,33(5):898-916. [22]VINCENT L,SOILLE P.Watersheds in Digital Spaces:An Efficient Algorithm Based on Immersion Simulations[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1991,13(6):583-598. [23]ZHANG C,XUE Z,ZHU X,et al.Boosted random contextualsemantic space based representation for visual recognition[J].Information Sciences,2016,369(6):160-170. [24]PONT-TUSET J,ARBELAEZ P,BARRON J T,et al.Multis-cale Combinatorial Grouping for Image Segmentation and Object Proposal Generation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,39(1):128-140. [25]FARABET C,COUPRIE C,NAJMAN L,et al.Learning Hierarchical Features for Scene Labeling[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2013,35(8):1915-1929. [26]GHIASI G,FOWLKES C C.Laplacian pyramid reconstructionand refinement for semantic segmentation[C]//European Conference on Computer Vision.Amsterdam:Springer,2016:519-534. [27]FAVREAU J D,LAFARGE F,BOUSSEAU A,et al.Extrac-ting Geometric Structures in Images with Delaunay Point Processes[C]//IEEE Transactions on Pattern Analysis and Machine Intelligence.IEEE,2019:1-1. [28]COUPRIE C,FARABET C,NAJMAN L,et al.Indoor Semantic Segmentation using depth information[J].arXiv preprint arXiv:1301.3572,2013. [29]LECUN Y,BOTTOU L,BENGIO Y,et al.Gradient-basedlearning applied to document recognition[J].Proceedings of the IEEE,1998,86(11):2278-2324. [30]KRIZHEVSKY A,SUTSKEVER I,HINTON G E.Imagenet classification with deep convolutional neural networks[C]//Advances in neural information processing systems.Nevada:ACM,2012:1097-1105. [31]RUSSAKOVSKY O,DENG J,SU H,et al.Imagenet large scale visual recognition challenge[J].International journal of computer vision,2015,115(3):211-252. [32]SIMONYAN K,ZISSERMAN A.Very Deep Convolutional Networks for Large-Scale Image Recognition[J].arXiv preprint arXiv:1409.1556,2014. [33]LIU Y,YU J,HAN Y.Understanding the effective receptivefield in semantic image segmentation[J].Multimedia Tools and Applications,2018,77(17):22159-22171. [34]SZEGEDY C,LIU W,JIA Y,et al.Going deeper with convolutions[C]//IEEE Conference on Computer Vision and Pattern Recognition.Boston:IEEE,2015:1-9. [35]HE K,ZHANG X,REN S,et al.Deep Residual Learning for Im-age Recognition[C]//IEEE Conference on Computer Vision and Pattern Recognition.Las Vegas:IEEE,2016:770-778. [36]LONG J,SHELHAMER E,DARRELL T.Fully Convolutional Networks for Semantic Segmentation[C]//IEEE Conference on Computer Vision and Pattern Recognition.Massachusetts:IEEE,2015:3431-3440. [37]BADRINARAYANAN V,KENDALL A,Cipolla R.Segnet:A deep convolutional encoder-decoder architecture for image segmentation[J].arXiv preprint arXiv:1511.00561,2015. [38]CHEN L-C,PAPANDREOU G,KOKKINOS I,et al.DeepLab:Semantic Image Segmentation with Deep Convolutional Nets,Atrous Convolution,and Fully Connected CRFs[J].IEEE transactions on pattern analysis and machine intelligence,2017,40(4):834-848. [39]LIN G,MILAN A,SHEN C,et al.RefineNet:Multi-Path Re-finement Networks for High-Resolution Semantic Segmentation[C]//IEEE Conference on Computer Vision and Pattern Recognition.Hawaii:IEEE,2017:5168-5177. [40]ZHAO H,SHI J,QI X,et al.Pyramid Scene Parsing Network[C]//IEEE Conference on Computer Vision and Pattern Recognition.Hawaii:IEEE,2017:6230-6239. [41]YU C,WANG J,PENG C,et al.BiSeNet:Bilateral Segmentation Network for Real-Time Semantic Segmentation[C]//European Conference on Computer Vision.Cham:Springer,2018:334-349. [42]CHEN L C,ZHU Y,PAPANDREOU G,et al.Encoder-decoder with atrous separable convolution for semantic image segmentation[J].arXiv preprint arXiv:1802.02611,2018. [43]CHEN L C,PAPANDREOU G,KOKKINOS I,et al.Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs[J].arXiv preprint arXiv:1412.7062,2014. [44]ZHENG S,JAYASUMANA S,ROMERA-PAREDES B,et al.Conditional Random Fields as Recurrent Neural Networks[C]//IEEE International Conference on Computer Vision.Santiago:IEEE,2015:1529-1537. [45]NOH H,HONG S,HAN B.Learning deconvolution network for semantic segmentation[C]//IEEE International Conference on Computer Vision.Santiago,Chile:IEEE,2015:1520-1528. [46]HONG S,NOH H,HAN B.Decoupled Deep Neural Networkfor Semi-supervised Semantic Segmentation[C]//Neural Information Processing Systems.Montreal:IEEE,2015:1495-1503. [47]PASZKE A,CHAURASIA A,KIM S,et al.Enet:A deep neural network architecture for real-time semantic segmentation[J].arXiv preprint arXiv:1606.02147,2016. [48]YANG J,PRICE B,COHEN S,et al.Object contour detection with a fully convolutional encoder-decoder network[C]//IEEE Conference on Computer Vision and Pattern Recognition.Las Vegas:IEEE,2016:193-202. [49]CHEN L C,PAPANDREOU G,SCHROFF F,et al.Rethinking Atrous Convolution for Semantic Image Segmentation[J].arXiv preprint arXiv:1706.05587,2017. [50]YU F,KOLTUN V.Multi-Scale Context Aggregation by Dilated Convolutions[J].arXiv:1511.07122,2015. [51]ZHOU S,WU J N,WU Y,et al.Exploiting Local Structureswith the Kronecker Layer in Convolutional Networks[J].arXiv preprint arXiv:1512.09194,2015. [52]WANG P,CHEN P,YUAN Y,et al.Understanding convolution for semantic segmentation[C]//IEEE Winter Conference on Applications of Computer Vision.Nevada:IEEE,2018:1451-1460. [53]LIN G,MILAN A,SHEN C,et al.Refinenet:Multi-path refinement networks for high-resolution semantic segmentation[C]//IEEE Conference on Computer Vision and Pattern Recognition.Hawaii:IEEE,2017:5168-5177. [54]ZHAO H,SHI J,QI X,et al.Pyramid scene parsing network[C]//IEEE Conference on Computer Vision and Pattern Recognition.Hawaii:IEEE,2017:2881-2890. [55]YU C,WANG J,PENG C,et al.Learning a Discriminative Feature Network for Semantic Segmentation[J].arXiv preprint arXiv:1804.09337,2018. [56]WOO S,PARK J,LEE J Y,et al.Cbam:Convolutional block attention module[C]//European Conference on Computer Vision.Cham:Springer,2018:3-19. [57]ZHANG H,DANA K,SHI J,et al.Context encoding for semantic segmentation[C]//IEEE Conference on Computer Vision and Pattern Recognition.Salt Lake City:IEEE,2018:7151-7160. [58]KIRILLOV A,GIRSHICK R,HE K,et al.Panoptic FeaturePyramid Networks[J].arXiv preprint arXiv:1901.02446,2019. [59]WEI Y,LIANG X,CHEN Y,et al.Learning to segment withimage-level annotations[J].Pattern Recognition,2016,59(1):234-244. [60]WEI Y,LIANG X,CHEN Y,et al.Stc:A simple to complex framework for weakly-supervised semantic segmentation[J].IEEE transactions on pattern analysis and machine intelligence,2017,39(11):2314-2320. [61]ZHOU B,KHOSLA A,LAPEDRIZA A,et al.Learning deepfeatures for discriminative localization[C]//IEEE Conference on Computer Vision and Pattern Recognition.Las Vegas:IEEE,2016:2921-2929. [62]WEI Y,FENG J,LIANG X,et al.Object region mining with adversarial erasing:A simple classification to semantic segmentation approach[C]//IEEE Conference on Computer Vision and Pattern Recognition.Hawaii:IEEE,2017:6488-6496. [63]ZHANG X,WEI Y,FENG J,et al.Adversarial complementary learning for weakly supervised object localization[C]//IEEE Conference on Computer Vision and Pattern Recognition.Salt Lake City:IEEE,2018:1325-1334. [64]RICHTER S R,VINEET V,ROTH S,et al.Playing for data:Ground truth from computer games[C]//European Conference on Computer Vision.Amsterdam:Springer,2016:102-118. [65]YAO T,PAN Y,NGO C W,et al.Semi-supervised domain adaptation with subspace learning for visual recognition[C]//IEEE Conference on Computer Vision and Pattern Recognition.Boston:IEEE,2015:2142-2150. [66]SUN B,FENG J,SAENKO K.Return of frustratingly easy domain adaptation[C]//The Thirty-Second AAAI Conference on Artificial Intelligence.Arizona:ACM,2016:2058-2065. [67]TZENG E,HOFFMAN J,ZHANG N,et al.Deep domain confusion:Maximizing for domain invariance[J].arXiv preprint arXiv:1412.3474,2014. [68]TZENG E,HOFFMAN J,DARRELL T,et al.Simultaneousdeep transfer across domains and tasks[C]//IEEE International Conference on Computer Vision.Santiago:IEEE,2015:4068-4076. [69]TZENG E,HOFFMAN J,SAENKO K,et al.Adversarial dis-criminative domain adaptation[C]//IEEE Conference on Computer Vision and Pattern Recognition.Hawaii:IEEE,2017:4. [70]HOFFMAN J,WANG D,YU F,et al.Fcns in the wild:Pixel-level adversarial and constraint-based adaptation[J].arXiv preprint arXiv:1612.02649,2016. [71]ZHANG Y,QIU Z,YAO T,et al.Fully Convolutional Adaptation Networks for Semantic Segmentation[C]//IEEE Conference on Computer Vision and Pattern Recognition.Salt Lake City:IEEE,2018:6810-6818. [72]BROSTOW G J,SHOTTON J,FAUQUEUR J,et al.Segmentation and Recognition Using Structure from Motion Point Clouds[C]//European Conference on Computer Vision.Marseille:Springer,2008:44-57. [73]BROSTOW G J,FAUQUEUR J,CIPOLLA R.Semantic object classes in video:A high-definition ground truth database[J].Pattern Recognition Letters,2009,30(2):88-97. [74]LIU C,YUEN J,TORRALBA A.Sift flow:Dense correspondence across scenes and its applications[J].IEEE transactions on pattern analysis and machine intelligence,2011,33(5):978-994. [75]RUSSELL B C,TORRALBA A,MURPHY K P,et al.La-belMe:A Database and Web-Based Tool for Image Annotation[J].International Journal of Computer Vision,2008,77(1/2/3):157-173. [76]EVERINGHAM M,ESLAMI S M A,GOOL L J V,et al.The Pascal Visual Object Classes Challenge:A Retrospective[J].International Journal of Computer Vision,2015,111(1):98-136. [77]MOTTAGHI R,CHEN X,LIU X,et al.The Role of Context for Object Detection and Semantic Segmentation in the Wild[C]//IEEE Conference on Computer Vision and Pattern Recognition.Columbus:IEEE,2014:891-898. [78]CORDTS M,OMRAN M,RAMOS S,et al.The CityscapesDataset for Semantic Urban Scene Understanding[C]//IEEE Conference on Computer Vision and Pattern Recognition.Las Vegas:IEEE,2016:3213-3223. [79]ROS G,SELLART L,MATERZYNSKA J,et al.The SYNTHIA Dataset:A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes[C]//IEEE Conference on Computer Vision and Pattern Recognition.Las Vegas:IEEE,2016:3234-3243. [80]HERNANDEZ-JUAREZ D,SCHNEIDER L,ESPINOSA A,et al.Slanted Stixels:Representing San Francisco’s Steepest Streets[J].arXiv:1707.05397,2017. [81]SILBERMAN N,HOIEM D,KOHLI P,et al.Indoor segmentation and support inference from rgbd images[C]//European Conference on Computer Vision.Florence:Springer,2012:746-760. [82]XIAO J,OWENS A,TORRALBA A.Sun3d:A database of big spaces reconstructed using sfm and object labels[C]//IEEE International Conference on Computer Vision.Sydney,Australia:IEEE,2013:1625-1632. [83]SONG S,LICHTENBERG S P,XIAO J.SUN RGB-D:A RGB-D scene understanding benchmark suite[C]//IEEE Conference on Computer Vision and Pattern Recognition.Massachusetts:IEEE,2015:567-576. [84]JANOCH A,KARAYEV S,JIA Y,et al.A category-level 3-D object dataset:Putting the Kinect to work[C]//IEEE International Conference on Computer Vision.Barcelona:IEEE,2011:1168-1174. [85]STURGESS P,ALAHARI K,LADICKY L,et al.Combiningappearance and structure from motion features for road scene understanding[C]//British Machine Vision Conference.London:British Machine Vision Association,2009:7-10. [86]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[C]//IEEE International Conference on Computer Vision.Vancouver:IEEE,2001:416-425. |
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