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计算机硕士开题报告(2)

来源:学术堂 作者:蒋老师
发布于:2017-05-05 共12393字
 [19] LI Jia, WANG J. Automatic linguistic indexing of pictures by a statistical modelingapproach[J]. IEEE Trans on Pattern Analysis and Machine Intelligence, 2003,25(9): 1075一1088.
  [20] LUO Jie-bo, SAVAKIS A E, SINGHAL A. A Bayesian network-based frameworkfor semantic image understanding[J]. Pattern Re-cognition, 2005,38(6): 919-934[21」AKSOY S, KOPERSKIK, TUSK C, etal. Learning Bayesian classifiers for sceneclassification with a visual grammar[J].IEEE Trans on Geoscience and RemoteSensing, 2005,43(3): 581一589.
  [22] HAN Yu-tao,  QI Xiao-jun. A complementary  SVMs-based image annotationsystem[C].  Proc  of  International  Conference  on  Image  Processing.  2005:1185一1188.
  [23] GOH K S, CHANG E Y, LI Bei-tao. Using one-class and two-class SVMs formulticlass image annotation[J]. IEEE Trans on Knowledge and Data Engineering,2005,17(10): 1333一1346.
  [24] Liu W, Sun Y, Zhang H. MiAlbum-a system for home photo managemet using thesemi-automatic  image  annotation  approach[C].  Acm  Multimedia  Conference.MULTIlVIEDIA '00 Proceedings of the eighth ACM international conference onMultimedia, 2000:479-480.
  [25] He X, King O, Ma W Y, et al. Learning a semantic space from user's relevancefeedback for image retrieval[J]. Circuits&Systems for Video Technology IEEETransactions on, 2003, 13(1):39-48.
  [26] Junwei H, Ngan K N, Mingjing L, et al. A memory learning framework foreffective image retrieval. [J]. IEEE Transactions on Image Processing A Publicationof the IEEE Signal Processing Society, 2005, 14(4):511一524.
  [27] SHENHeng-tao, OOIB C, TANK L. Givingmeanings to WWW im-ages[C]. Procof the 8thACM International Conference on Multime-dia. New York: ACM Press,2000: 39-47.
  [28] YANG H  C,  LEE  C H.  Image  semantics  discovery  from  Web pages  forsemantic-based image retrieval using self-organizing maps[J]. Expert Systems withApplications, 2008,34(1): 266-279.
  [29] Ames, Morgan, Naaman, Mor. Why we tag: motivations for annotation in mobileand online media[C]. Proceedings of the SIGCHI Conference on Human Factors inComputing Systems. ACM, 2007:971一980.
  [30] Rattenbury T, Good N, Naaman M. Towards automatic extraction of event andplace semantics from flickr tags[C]. Proceedings of the 30th annual internationalACM SIGIR conference on Research and development in information retrieval.ACM, 2007:103一110.
  [31]朱蓉。基于语义信息的图像理解关键问题研究[J].计算机应用研究,2009,26(4): 1234:1240.
  [32] Hinton G E,Salakhutdinov R R. Reducing the dimensionality of data with neuralnetworks[J]. Science, 2006,  313(5786): 504-507.
  [33] Hinton G E, Osindero S,Teh Y W. A fast learning algorithm for deep belief nets[J].Neural Computation, 2006,  18(7): 1527-1554.
  [34] Vincent P, Larochelle H, Lajoie I, et al. Stacked denoising autoencoders: Learninguseful representations in a deep network with a local denoising criterion[J], TheJournal of Machine Learning Research, 2010, 9999: 3371-3408.
  [35] Lee H,Grosse R, Ranganath R,et al. Convolutional deep belief networks forscalable unsupervised learning of hierarchical representations[C]. The 26th AnnualInternational Conference on Machine Learning (ICML 2009)。 Montreal: ACM,2009: 609-616.
  [36] Markoff J. How many computers to identi勿a cat? [N] . The New York Times, 2012.
  [37] Krizhevsky A,  Sutskever I, Hinton G E. ImageNet Classification with DeepConvolutional  Neural  Networks[C].  2012  Advances  in  Neural  InformationProcessing Systems(NIPS 2012)。 Lake Tahoe: NIPS foundation, 2012, 1(2): 4.
  [38]李彦宏。2012百度年会主题报告:相信技术的力量[R].北京:百度,2013.
  [39] Fan H, Cao Z, Jiang Y, et al. Learning Deep Face Representation[J]. Eprint Arxiv,2014.
  [40] Datta R, Joshi D, Li J, et al. Image retrieval: Ideas, influences, and trends of thenew age[J]. Acm Computing Surveys, 2008, 40(2):2007.
  [41」Lee, Honglak, Grosse, Roger, Ranganath, Rajesh, et al. Convolutional deep beliefnetworks for scalable unsupervised learning of hierarchical representations[C].InInternational Conference on Machine Learning. 2009:609-616.
       [42] Zeiler M D, Fergus R. Visualizing and Understanding Convolutional Networks[M].Computer Vision一ECCV 2014 Springer International Publishing, 2014:818一833.
  [43]马冬梅。基于深度学习的图像检索研究[[D].内蒙古大学,2014.5:  9-10.
  [44]夏定元。基于内容的图像检索通用技术研究及应用[D].华中科技大学,2004:46-47.
  [44] Moghaddam B, Pentland A. Probabilistic visual learning for object detection[C].Computer Vision, 1995. Proceedings., Fifth International Conference on. IEEE,1995:786-793.
  [45] Murphy K, Torralba A, Eaton D, et al. Object Detection and Localization UsingLocal and Global Features.
       [46]. Lecture Notes in Computer Science, 2006, 12(1):20一26.
  [47] D. Fox, L. Bo, X. Ren. Kernel Descriptors for Visual Recognition[J]. Advances inNeural Information Processing Systems, 2010.
  [48] Norbert, Kriiger, Peter, Janssen, Sinan, Kalkan, et al. Deep hierarchies in theprimate  visual  cortex:  what  can  we  learn  for  computer  vision?[J].  IEEETransactions on Software Engineering, 2013, 35(8):1847-1871.
  [49] Hubel D H, Wiesel T N. Receptive fields, binocular interaction and functionalarchitecture  in  the  cat's  visual  cortex. [J].  Journal  of  Physiology,  1962,160(1):106-154.
  [50] D. Marr,Vision. A Computational Investigation into the Human Representation andProcessing of Visual information[M]. Freeman, 1982.
  [51」Hinton G E, Osindero S, Teh Y W. A fast learning algorithm for deep belief nets. [J].Neural Computation, 2006, 18(7):1527-54.
  [52] Le, Q.V Building high-level features using large scale unsupervised learning[C].Acoustics,  Speech and Signal Processing (ICASSP), 2013 IEEE InternationalConference on. IEEE, 2011:8595一8598.
  [53] Krizhevsky A,  Sutskever I, Hinton G E. ImageNet Classification with DeepConvolutional Neural Networks[J]. Advances in Neural Information ProcessingSystems, 2012, 25:2012.
  [54] Bo L, Ren X, Fox D. Hierarchical Matching Pursuit for Image Classification:Architecture and Fast Algorithms[J]. Nips, 2011:2115-2123.
  [55] Yu K, Lin Y, Lafferty J. Learning image representations from the pixel level viahierarchical sparse coding[C]// Proceedings/CVPR, IEEE Computer SocietyConference on Computer Vision and Pattern Recognition. IEEE Computer SocietyConference on Computer Vision and Pattern Recognition. 2011:1713一1720.
    [56] Goh H, Thome N, Cord M, et al. Learning Deep Hierarchical Visual FeatureCoding[J]. IEEE Transactions on Neural Networks&Learning Systems, 2014,25(12):2212-25.
  [57] A. Coates and A. Y Ng. The importance of encoding versus training with sparsecoding  and  vector  quantization[J].  Proceedings  of  the  28th InternationalConference on Machine Learning, 2011.
  [58] Scherer D, Miiller A, Behnke S. Evaluation of Pooling Operations in ConvolutionalArchitectures for Object Recognition.[M]. Artificial Neural Networks2010. Springer Berlin Heidelberg, 2010:92-101.ICANN
       [59] Bengio Y Learning Deep Architectures for AI[J]. Foundations&Trends. inMachine Learning, 2009, 2(1):1一127.
  [60] Lee, Honglak, Grosse, Roger, Ranganath, Rajesh, et al. Convolutional deep beliefnetworks for scalable unsupervised learning of hierarchical representations[C]. InInternational Conference on Machine Learning. 2009:609-616.
  [61」Krizhevsky A,  Sutskever I, Hinton G E. ImageNet Classification with DeepConvolutional Neural Networks[J]. Advances in Neural Information ProcessingSystems, 2012, 25:2012.
  [62] Miclut B. Committees of deep feedforward networks trained with few data[J].Lecture Notes in Computer Science, 2014, 8753:736-742.
  [63] Bo L, Ren X, Fox D. Unsupervised Feature Learning for RGB-D Based ObjectRecognition[J]. Springer Tracts in Advanced Robotics, 2013, 88:387-402.
  [64] Mairal J, Koniusz P, Harchaoui Z, et al.  Convolutional Kernel Networks[J].Advances in Neural Information Processing Systems, 2014:2627-2635.
  [65] Romero A, Radeva P, Gatta C. Meta-Parameter Free Unsupervised Sparse FeatureLearning[J]. IEEE Transactions on Pattern Analysis&Machine Intelligence, 2015,37(8):1716-1722.
  [66] Li J, Wang J Z. Automatic Linguistic Indexing of Pictures by a statistical modelingapproach[J]. Pattern Analysis&Machine Intelligence IEEE Transactions on, 2003,25(9):1075一1088.
  [67] Chang E, Goh K, Sychay G, et al. CBSA:content-based soft annotation formultimodal image retrieval using bayes point machines[J]. IEEE Transactions onCircuits&Systems for Video Technology, 2003, 13(1):26-38.
  [68」Duygulu P, Barnard K, Freitas J F G D, et al. Object Recognition as MachineTranslation: Learning a Lexicon for a Fixed Image Vocabulary[C]. Proceedings ofthe  7th European  Conference  on  Computer  Vision-Part IV.  Springer-Verlag,2002:97-112.
  [69] Feng S L, Manmatha R, Lavrenko V Multiple Bernoulli relevance models forimage and video annotation[C]. Computer Vision and Pattern Recognition, 2004.CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on.IEEE, 2004:II-1002-II-1009 Vo1.2.
  [70] Jeon, J, Lavrenko, V, Manmatha, R. Automatic Image Annotation and Retrievalusing Cross-Media Relevance Models[C]. Proceedings of the 26th InternationalACM SIGIR Conference SIGIR 2003, ACM 2003. 2003:119-126.
  [71] Gustavo, Carneiro, Antoni B, Chan, Pedro J, Moreno, et al. Supervised learning ofsemantic classes for image annotation and retrieval. [J]. IEEE Transactions onPattern Analysis&Machine Intelligence, 2007, 29(3):394-410.
  [72] Lavrenko V, Manmatha R, Jeon J. A Model for Learning the Semantics ofPictures[J]. Nips, 2003:553一560.
  [73] Y Mori, H.  Takahashi, and R. Oka. Image-to-word transformation based ondividing and vector quantizing images with words[C]. MISRII,1999:405-409.
  [74] Changhu Wang, Shuicheng Yan, Lei Zhang, et al. Multi-label sparse coding forautomatic image annotation[M]. Multi. IEEE, 2009:1643一1650.
  [75] Liu Y, Yang F. Automatic image annotation based on scene semantic trees[J].Journal of Image&Graphics, 2013.
  [76] Makadia A, Pavlovic V, Kumar S. A New Baseline for Image Annotation[M].Computer Vision一ECCV 2008. Springer Berlin Heidelberg, 2008:316-329.
  [77]  Guillaumin M, Mensink T, Verbeek J, et al.  TagProp: Discriminative metriclearning in nearest neighbor models for image auto-annotation[C].  ComputerVision, 2009 IEEE 12th International Conference on. IEEE, 2009:309-316.

 

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