开题报告

您当前的位置:学术堂 > 开题报告 >

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.

 

相关内容推荐
  • 世界文学硕士论文开题报告精选

    歌德提出世界文学的观念差不多近两个世纪了,这是中国比较文学界的各种论文里都会提到的一个观念,让人生出无限倦意。在世界文学论文写作之前通常还需要提交一份开题报告,以下世界文学硕士论文开题报告就是其中的范例之一。题目:徐訏与法国浪漫主义文学...

  • 计算机研究生开题报告范文

    计算机研究生开题报告包括计算机系统结构、计算机软件与理论、计算机应用技术三个方向,不同计算机硕士专业研究主题各有特点,其开题报告形式却大体一致,下文以“PHP技术应用于中小企业网站开发”课题为例,拟定计算机研究生开题报告范文。...

  • 天线硕士开题报告

    我们知道,通信、雷达、导航、广播、电视等无线电设备,都是通过无线电波来传递信息的,都需要有无线电波的辐射和接收。在无线电设备中,用来辐射和接收无线电波的装置称为天线。天线为发射机或接收机与传播无线电波的媒质之间提供所需要的耦合。天线和发射...

  • 优秀法学硕士毕业论文开题报告范文

    法律不是高高在上的东西,它关系着你我,影响了人们日常的生活。记下来学术堂以一篇范文来告诉你法学硕士开题报告的写作方法。题目:夫妻财产关系法律适用研究一、问题的提出和研究意义夫妻财产关系又称夫妻财产制,是指由夫妻人身关系所引起的直接体现...

  • 硕士汉语言文字学开题报告《儿女英雄传》

      开题报告是整个毕业设计的开始环节,活动设计贯穿整个流程,严重的来说,考题报告好坏能够影响整个毕业论文设计的成败,下面学术堂以浙江大学硕士生开题报告为例,整理出一篇开题报告范文,供各位同学查看。 ...

  • 最新农业硕士开题报告格式

    农业推广硕士是与特定的职业背景相联系的,与相应学科的农学硕士学位处于同一层次的新的学位类型,主要培养高层次应用型、符合型人才,是农业的MBA.下面是我们整理的农业硕士开题报告格式,供你参考借鉴。引言(或前言)主要介绍课题的立题背景,简单提到...

  • 怎样写好研究生毕业论文开题报告

    在研究生教育的整个过程中,学位论文质量的高低是衡量研究生培养质量的重要标志。而论文质量的高低,很大程度上取决于开题报告做的细致程度。开题报告做的细致,前期虽然花费的时间较多,但写起论文来就很顺手,能够做到胸有成竹,从而保证论文在规定的时间...

  • 学前教育硕士开题报告

    儿童是人生智力发展的基础阶段,又是发展最快的时期,适当、正确的学前教育对幼儿智力及其日后的发展有很大的作用。超常儿童的形成、发展,无一不与适当、正确的学前教育有关,尤其是智力方面的学前教育。以下是学前教育硕士开题报告,希望能够帮助大家。题...

  • 教育学硕士开题报告

    教育学是一门研究教育现象及其规律的社会科学。它广泛存在于人类生活中。通过对教育现象、教育问题的研究来揭示教育的一般规律。教育学的任务就是要探讨、揭示种种教育的规律,阐明各种教育问题,建立教育学理论体系。以下是教育学硕士开题报告,供大家参考...

  • 专业硕士开题报告

    硕士研究生学位论文开题报告是整个学位论文顺利进行的必要基础,是保证学位论文质量的重要环节,专业硕士研究生、导师和学生所在单位应给予充分的重视。以下是专业硕士开题报告,供大家参考。专业硕士开题报告范例题目:道路施工项目成本管理应用的研究...

相关标签:硕士论文开题报告
返回:开题报告