Computer Science ›› 2019, Vol. 46 ›› Issue (9): 36-46.doi: 10.11896/j.issn.1002-137X.2019.09.005

• Surverys • Previous Articles     Next Articles

Research on Image Semantic Segmentation for Complex Environments

WANG Yan-ran1, CHEN Qing-liang1 , WU Jun-jun2   

  1. (College of Information Science and Technology,Jinan University,Guangzhou 510632,China)1;
    (School of Mechatronics Engineering,Foshan University,Foshan,Guangdong 528225,China)2
  • Received:2019-02-17 Online:2019-09-15 Published:2019-09-02

Abstract: Image semantic segmentation is one of the most important fundamental technologies for visual intelligence.Semantic segmentation can greatly enable intelligent systems to understand their surrounding scenarios,so it has enormous value in application domains such as unmanned vehicles,robot cognition and navigation,video surveillance and drone landing systems.Great challenges also exist in the semantic segmentation of images,due to various interfering factors of targets in complex environments,such as unstructured targets,diversity of objectives,irregular shapes,illumination changes,different viewing angles,scale variation,object occlusion,etc.In recent years,benefiting from the great advancements in deep learning techniques,a large number of research approaches with practical significance emerge in ima-ge semantic segmentation.For having a comprehensive survey and inspiring the academic research,this paper extensively discussed the existing state-of-the-art image semantic segmentation methods,and further classified them into the traditional image semantic segmentation ones,the ones combining traditional and deep learning techniques,and those based purely on deep learning.In order to address these problems in complex environments,various semantic segmentation methods for complex environment emerged in recent years were analyzed and compared in detail,including the mo-dels,algorithms and performance with the category of strong supervised,weak supervised and unsupervised semantic segmentation methods.Furthermore,the current main datasets such as PASCAL VOC,Cityscape,SUN RGB-D,which contains various complex environments and 3 evaluation indicators of PA,mPA,mIoU were summarized.Finally,the existing research of image semantic segmentation for complex environment was summarized,and its future trends were prospected such as optimization in real-time video,3d scene reconstruction and unsupervised semantic segmentation techniques.

Key words: Semantic segmentation, Visual intelligence, Deep learning, Image segmentation, Convolutional neural network

CLC Number: 

  • TP391
[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.
[1] ZHOU Yan, ZENG Fan-zhi, WU Chen, LUO Yue, LIU Zi-qin. 3D Shape Feature Extraction Method Based on Deep Learning [J]. Computer Science, 2019, 46(9): 47-58.
[2] MA Lu, PEI Wei, ZHU Yong-ying, WANG Chun-li, WANG Peng-qian. Fall Action Recognition Based on Deep Learning [J]. Computer Science, 2019, 46(9): 106-112.
[3] LI Qing-hua, LI Cui-ping, ZHANG Jing, CHEN Hong, WANG Shao-qing. Survey of Compressed Deep Neural Network [J]. Computer Science, 2019, 46(9): 1-14.
[4] SUN Zhong-feng, WANG Jing. RCNN-BGRU-HN Network Model for Aspect-based Sentiment Analysis [J]. Computer Science, 2019, 46(9): 223-228.
[5] MIAO Yong-wei, LI Gao-yi, BAO Chen, ZHANG Xu-dong, PENG Si-long. Image Localized Style Transfer Based on Convolutional Neural Network [J]. Computer Science, 2019, 46(9): 259-264.
[6] SHI Xiao-hong, HUANG Qin-kai, MIAO Jia-xin, SU Zhuo. Edge-preserving Filtering Method Based on Convolutional Neural Networks [J]. Computer Science, 2019, 46(9): 277-283.
[7] DENG Cun-bin, YU Hui-qun, FAN Gui-sheng. Integrating Dynamic Collaborative Filtering and Deep Learning for Recommendation [J]. Computer Science, 2019, 46(8): 28-34.
[8] DU Wei, DING Shi-fei. Overview on Multi-agent Reinforcement Learning [J]. Computer Science, 2019, 46(8): 1-8.
[9] GUO Xu, ZHU Jing-hua. Deep Neural Network Recommendation Model Based on User Vectorization Representation and Attention Mechanism [J]. Computer Science, 2019, 46(8): 111-115.
[10] ZHANG Yi-jie, LI Pei-feng, ZHU Qiao-ming. Event Temporal Relation Classification Method Based on Self-attention Mechanism [J]. Computer Science, 2019, 46(8): 244-248.
[11] YU Yang, LI Shi-jie, CHEN Liang, LIU Yun-ting. Ship Target Detection Based on Improved YOLO v2 [J]. Computer Science, 2019, 46(8): 332-336.
[12] LI Zhou-jun,WANG Chang-bao. Survey on Deep-learning-based Machine Reading Comprehension [J]. Computer Science, 2019, 46(7): 7-12.
[13] ZHANG Lin-na,CHEN Jian-qiang,CHEN Xiao-ling,CEN Yi-gang,KAN Shi-chao. Lightweight SSD Network for Real-time Object Detection in Automotive Videos [J]. Computer Science, 2019, 46(7): 233-237.
[14] LI Jian, YANG Xiang-ru, HE Bin. Geometric Features Matching with Deep Learning [J]. Computer Science, 2019, 46(7): 274-279.
[15] KONG Fan-yu, ZHOU Yu-feng, CHEN Gang. Traffic Flow Prediction Method Based on Spatio-Temporal Feature Mining [J]. Computer Science, 2019, 46(7): 322-326.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75, 88 .
[2] SUN Qi, JIN Yan, HE Kun and XU Ling-xuan. Hybrid Evolutionary Algorithm for Solving Mixed Capacitated General Routing Problem[J]. Computer Science, 2018, 45(4): 76 -82 .
[3] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[4] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[5] GENG Hai-jun, SHI Xin-gang, WANG Zhi-liang, YIN Xia and YIN Shao-ping. Energy-efficient Intra-domain Routing Algorithm Based on Directed Acyclic Graph[J]. Computer Science, 2018, 45(4): 112 -116 .
[6] WANG Zhen-chao, HOU Huan-huan and LIAN Rui. Path Optimization Scheme for Restraining Degree of Disorder in CMT[J]. Computer Science, 2018, 45(4): 122 -125 .
[7] ZHU Shu-qin, WANG Wen-hong and LI Jun-qing. Chosen Plaintext Attack on Chaotic Image Encryption Algorithm Based on Perceptron Model[J]. Computer Science, 2018, 45(4): 178 -181, 189 .
[8] TONG Ze-ping, LI Tao, LI Li-jie and REN Liang. Study on Collaborative Optimization of Supply Chain with Uncertain Demand and Capacity Constraint[J]. Computer Science, 2018, 45(4): 260 -265 .
[9] HOU Lin-qing, CAI Ying, FAN Yan-fang, XIA Hong-ke. Interest Community Based Message Transmission Scheme in Mobile Social Networks[J]. Computer Science, 2018, 45(6): 105 -110 .
[10] WANG Qian, YU Lai-hang, CAO Yan, ZHANG Lei, QIN Jie, YE Hai-qin. Blind Watermarking Algorithm for Digital Image Based on Fibonacci Scrambling in Wavelet Domain[J]. Computer Science, 2018, 45(6): 135 -140 .