在线阅读 --自然科学版 2019年4期《基于块模型的目标检测综述》
基于块模型的目标检测综述--[在线阅读]
霍丽娜, 黄文艳, 王国强, 李瑞台, 高静伟
河北师范大学 数学与信息科学学院, 河北 石家庄 050024
起止页码: 298--305页
DOI: 10.13763/j.cnki.jhebnu.nse.2019.04.004
摘要
目标检测是计算机视觉研究领域的一个重要问题,在图像检索和视频监控等方面具有重要的研究意义和广阔的应用前景.综述了块模型在目标检测中的应用进展和展望,首先,对目标检测的基本表示方法进行总结;然后重点介绍了块模型在目标检测中的应用进展;最后,讨论了块模型在目标检测中存在的困难和挑战.

A Survey of Block Model for Object Detection
HUO Lina, HUANG Wenyan, WANG Guoqiang, LI Ruitai, GAO Jingwei
College of Mathematics and Information Sciences, Hebei Normal University, Hebei Shijiazhuang 050024, China
Abstract:
Image object detection is an important topic in computer vision,and has great theoretical and practical merits in applications such as image retrieval and visual surveillance.In this paper,we review the current advances and perspectives on the applications of block model in visual object detection.Firstly,we present the basic representation for object detection.Then,we detail the applications of block model in visual object detection.Finally,we make indepth discussions about the difficulties and challenges brought by block model as applied to visual object detection.

收稿日期: 2018-09-15
基金项目: 国家自然科学青年基金(61702158);河北省自然科学基金(F2018205137)

参考文献:
[1]YANG M,KRIEGMAN D J,AHUJA N.Detecting Faces in Images:A Survey[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(1):34-58.doi:org/10.1109/34.982883
[2]SUKANYA C M, GOKULL R, PAUL V.A Survey on Object Recognition Methods[J].International Journal of Science,Engineering and Computer Technology,2016,6(1):69-72.
[3]GALLEGUILLOS C,BELONGIE S J.Context Based Object Categorization:A Critical Survey[J].Computer Vision and Image Understanding,2010,114(6):712-722.doi:org/10.1016/j.cviu.2010.02.004
[4]LIN T,GOYALL P,GIRSHICK R B,et al.Focal Loss for Dense Object Detection[C]//IEEE International Conference on Computer Vision,2017:2999-3007.doi:org/10.1109/Iccv.2017.324
[5]DALAL N,TRIGGS B.Histograms of Oriented Gradients for Human Detection[C]//IEEE Conference on Computer Vision and Pattern Recognition,2005:886-893.doi:org/10.1109/iraniancee.2013.6599619
[6]NGPAULINE C,HENIKOFF S.SIFT:Predicting Amino Acid Changes That Affect Protein Function[J].Nucleic Acids Research,2003,31(13):3812-3814.doi:org/10.1093/Nar/Gkg509
[7]CSURKA G,DANCE C R,FAN L,et al.Visual Categorization with Bags of Keypoints[C]//European Conference on Computer Vision.Berlin:Springer,2004:1-22.
[8]LI F,PERONA P.A Bayesian Hierarchical Model for Learning Natural Scene Categories[C]//IEEE Conference on Computer Vision and Pattern Recognition,2005:524-531.doi:org/10.1109/cvpr.2005.16
[9]CRANDALLL D J,FELZENSZWALB P F,HUTTENLOCHER D P.Spatial Priors for Part-based Recognition Using Statistical Models[C]//IEEE Conference on Computer Vision and Pattern Recognition,2005:10-17.doi:org/10.1109/cvpr.2005.329
[10]CARNEIRO G,LOWE D B.Sparse Flexible Models of Local Features[C]//European Conference on Computer Vision.Berlin:Springer,2006:29-43.doi:org/10.1007/11744078_3
[11]FERGUS R,PERONA P,ZISSERMAN A.Object Class Recognition by Unsupervised Scale-invariant Learning[C]//IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2003:264-271.doi:org/10.1109/cvpr.2003.1211479
[12]LI F,FERGUSV R,PERONA P.A Bayesian Approach to Unsupervised One-shot Learning of Object Categories[C]//Proceedings of Ninth IEEE International Conference on Computer Vision,2003:1134-1141.doi:org/10.1109/iccv.2003.1238476
[13]LEIBE B,LEONARDIS A,SCHIELE B.Combined Object Categorization and Segmentation with an Implicit Shape Model[J].Workshop on Statistical Learning in Computer Vision,2004,2(5):7-23.doi:org/10.1007/11957959_26
[14]FELZENSZWALB P,MCALLESTER D,RAMANAN D.A Discriminatively Trained,Multiscale,Deformable Part Model[C]//IEEE Conference on Computer Vision and Pattern Recognition,2008:1-8.doi:org/10.1109/cvpr.2008.4587597
[15]ZHU L,CHEN Y,YUILLE A,et al.Latent Hierarchical Structural Learning for Object Detection[C]//IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2010:1062-1069.doi:org/10.1109/cvpr.2010.5540096
[16]ZHU L,CHEN Y,YUILLE A.Learning A Hierarchical Deformable Template for Rapid Deformable Object Parsing[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2010,32(6):1029-1043.doi:org/10.1109/tpami.2009.65
[17]PEDERSOLI M,VEDALDI A,GONZALEZ J.A Coarse-to-fine Approach for Fast Deformable Object Detection[C]//IEEE Conference on Computer Vision and Pattern Recognition,2011:1353-1360.doi:org/10.1109/cvpr.2011.5995668
[18]TORRALBA A.Contextual Priming for Object Detection[J].International Journal of Computer Vision,2003,53(2):169-191.doi:10.1023/A:1023052124951
[19]TORRALBA A,MURPHY K P,FREEMAN W T.Contextual Models for Object Detection Using Boosted Random Fields[C]//Advances in Neural Information Processing Systems.2005:1401-1408.
[20]WANG X, BAI X,LIU W,et al.Feature Context for Image Classification and Object Detection[C]//IEEE Conference on Computer Vision and Pattern Recognition,2011:961-968.doi:org/10.1109/cvpr.2011.5995696
[21]LAMPERT C H,BLASCHKO M B.A Multiple Kernel Learning Approach to Joint Multi-class Object Detection[C]//Joint Pattern Recognition Symposium.Berlin:Springer,2008:31-40.doi:org/10.1007/978-3-540-69321-5_4
[22]MOHAN A,PAPAGEORGIOU C,POGGIO T A.Example-based Object Detection in Images by Components[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2001,23(4):349-361.doi:org/10.1109/34.917571
[23]HOFMANN T.Probabilistic Latent Semantic Analysis[C]//Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence,1999:289-296.
[24]BLEI D M,NGANDREW Y,JORDAN M I.Latent Dirichlet Allocation[J].Journal of Machine Learning Research,2003,3:993-1022.
[25]BURL M C,WEBER M,PERONA P.A Probabilistic Approach to Object Recognition Using Local Photometry and Global Geometry[C]//European Conference on Computer Vision.Berlin:Springer,1998:628-641.doi:org/10.1007/Bfb0054769
[26]FELZENSZWALB P F,HUTTENLOCHER D P.Pictorial Structures for Object Recognition[J].International Journal of Computer Vision,2005,61(1):55-79.doi:org/10.1023/b:visi.0000042934.15159.49
[27]BERG A C,BERG T L,MALIK J.Shape Matching and Object Recognition Using Low Distortion Correspondences[C]//IEEE Conference on Computer Vision and Pattern Recognition,2005:26-33.doi:org/10.1109/cvpr.2005.320
[28]SCHOLKOPF B.Making Large Scale SVM Learning Practical[J].Advances in Kernel Methods:Support Vector Learning,1999(1):41-56.doi:org/10.7551/mitpress/7496.003.0007
[29]LAFFERTY J D,MCCALLUM A,PEREIRA F.Conditional Random Fields:Probabilistic Models for Segmenting and Labeling Sequence Data[C]//Proceedings of the 18th International Conference on Machine Learning,2001:282-289.
[30]BIEDERMAN I,Perceiving Real-world Scenes[J].Science,1972,177(7):77-80.doi:org/10.1126/science.177.4043.77
[31]FISCHLER M A,ELSCHLAGER R A.The Representation and Matching of Pictorial Structures[J].IEEE Transactions on Computers,1973,100(22):67-92.doi:org/10.1109/t-c.1973.223602
[32]HANSON A,RISEMAN E.Visions:A Computer System for Interpreting Scenes[J].Computer Vision Systems,1978,32(3):303-334.
[33]SINGHAL A,LUO J,ZHU W.Probabilistic Spatial Context Models for Scene Content Understanding[C]//IEEE Conference on Computer Vision and Pattern Recognition,2003:1-8.doi:org/10.1109/cvpr.2003.1211359
[34]RABINOVICH A,VEDALDI A,GALLEGUILLOS C,et al.Objects in Context[C]//IEEE 11th International Conference on Computer Vision,2007:1-8
[35]VERBEEK J J,TRIGGS B.Scene Segmentation with Crfs Learned from Partially Labeled Images[C]//Advances in Neural Information Processing Systems,2008:1553-1560.
[36]WOLF L,BILECHI S M.A Critical View of Context[J].International Journal of Computer Vision,2006,69(2):251-261.
[37]FINK M,PERONA P.Mutual Boosting for Contextual Inference[C]//Advances in Neural Information Processing Systems,2004:1515-1522.
[38]KRUPPA H,SCHIELEE B.Using Local Context to Improve Face Detection[C]//The British Machine Vision Conference,2003:1-10.doi:org/10.5244/c.17.5
[39]MURPHY K,TORRALBA A,FREEMAN W T.Using the Forest to See the Trees:A Graphical Model Relating Features,Objects,and Scenes[C]//Advances in Neural Information Processing Systems,2004:1499-1506.
[40]PARIKH D,ZITNICK C L,CHEN T.From Appearance to Context-based Recognition:Dense Labeling in Small Images[C]//IEEE Conference on Computer Vision and Pattern Recognition,2008:1-8.doi:org/10.1109/cvpr.2008.4587595
[41]RUSSELL B,TORRALBA A,LIU C,et al.Object Recognition by Scene Alignment[C]//Advances in Neural Information Processing Systems,2008:1241-1248..doi:org/10.1167/3.9.196
[42]KUMAR S,HEBERT M.A Hierarchical Field Framework for Unified Context-based Classification[C]//IEEE International Conference on Computer Vision,2005:1284-1291.doi:org/10.1109/iccv.2005.9
[43]GALLEGUILLOS C,RABINOVICH A,BELONGIE S.Object Categorization Using Co-occurrence,Location and Appearance[C]//IEEE Conference on Computer Vision and Pattern Recognition,2008:1-8.doi:org/10.1109/cvpr.2008.4587799
[44]CARBONETTO P,NANDO de F,KOBUS B.A Statistical Model for General Contextual Object Recognition[C]//European Conference on Computer Vision.Berlin:Springer,2004:350-362.doi:org/10.1007/978-3-540-24670-1_27
[45]LAMPERT C H,BLASCHKO M B,HOFMANN T.Beyond Sliding Windows:Object Localization by Efficient Subwindow Search[C]//IEEE Conference on Computer Vision and Pattern Recognition,2008:1-8.doi:org/10.1109/cvpr.2008.4587586
[46]LAZEBNIK S,SCHMID C,PONCE J.Beyond Bags of Features:Spatial Pyramid Matching for Recognizing Natural Scene Categories[C]//IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2006:2169-2178.doi:org/10.1109/cvpr.2006.68
[47]AN S,PEURSUM P,LIU W,et al.Efficient Algorithms for Subwindow Search in Object Detection and Localization[C]//IEEE Conference on Computer Vision and Pattern Recognition,2009:264-271.doi:org/10.1109/cvpr.2009.5206822
[48]BUTKO N J,JAVIER R M.Optimal Scanning for Faster Object Detection[C]//IEEE Conference on Computer Vision and Pattern Recognition,2009:2751-2758.doi:org/10.1109/cvpr.2009.5206540
[49]LEIBE B,SCHIELE B.Interleaving Object Categorization and Segmentation[M].Berlin:Springer,2006:145-161.doi:org/10.1007/11414353_10
[50]OPELT A,PINZ A,ZISSERMAN A.A Boundary-fragment-model for Object Detection[C]//European Conference on Computer Vision.Berlin:Springer,2006:575-588.doi:org/10.1007/11744047_44
[51]SUBHRANSU M,JITENDRA M.Object Detection Using a Max-margin Hough Transform[C]//IEEE Conference on Computer Vision and Pattern Recognition,2009:1038-1045.doi:org/10.1109/cvpr.2009.5206693
[52]GALL J,YAO A,RAZAVI N,et al.Hough Forests for Object Detection,Tracking and Action Recognition[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2011,33(11):2188-2202.doi:org/10.1109/tpami.2011.70
[53]VIOLA P A,JONES M J.Rapid Object Detection Using A Boosted Cascade of Simple Features[C]//IEEE Conference on Computer Vision and Pattern Recognition,2001:511-518.doi:org/10.1109/cvpr.2001.990517
[54]HEISELE B,SERRE T,PRENTIC S,et al.Hierarchical Classification and Feature Reduction for Fast Face Detection with Support Vector Machines[J].Pattern Recognition,2003,36(9):2007-2017.doi:org/10.1016/s0031-3203(03)00062-1
[55]LI STAN Z,ZHANG Z.Floatboost Learning and Statistical Face Detection[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2004,26(9):1112-1123.doi:org/10.1109/tpami.2004.68
[56]SCHAPIRE R E.The Boosting Approach to Machine Learning:An Overview[M].New York:Springer,2003:149-171.doi:org/10.1007/978-0-387-21579-2_9
[57]SCHNEIDERMAN H.Feature-centric Evaluation for Efficient Cascaded Object Detection[C]//Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2004:29-36.doi:org/10.1109/cvpr.2004.1315141
[58]WU J,REHG J M,MULLIN M D.Learning A Rare Event Detection Cascade by Direct Feature Selection[C]//Advances in Neural Information Processing Systems,2004:1523-1530.doi:org/10.1109/cvpr.2004.1315141
[59]SUN J,REHG J M,BOBICK A F.Automatic Cascade Training with PerturbationBias[C]//IEEE Conf Computer Vision and Pattern Recognition,2004:276-283.doi:org/10.1109/cvpr.2004.1315174