Benenson R, Mathias M, Timofte R, et al. Pedestrian Detection at 100 Frames per Second // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Providence, USA, 2012: 2903-2910
[2]
Zhang L, Van Der Maaten L. Structure Preserving Object Tracking // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Portland, USA, 2013: 1838-1845
[3]
Wang Q Z, Kang W W, Wang B. Design of 3D Latent-SVM and Application to Detection of Lesions in Chest CT. Pattern Recognition and Artificial Intelligence, 2013, 26(5): 460-466 (in Chinese) (王青竹,康文炜,王 斌.三维隐SVM算法设计及在胸CT图像病灶检测中的应用.模式识别与人工智能, 2013, 26(5): 460-466)
[4]
Wang G, Forsyth D, Hoiem D. Comparative Object Similarity for Improved Recognition with Few or No Examples // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. San Francisco, USA, 2010: 3525-3532
[5]
Oyen D, Lane T. Leveraging Domain Knowledge in Multitask Bayesian Network Structure Learning // Proc of the 26th AAAI Conference on Artificial Intelligence. Toronto, Canada, 2012: 1091-1097
[6]
Li L J, Socher R, Li F F. Towards Total Scene Understanding: Classification, Annotation and Segmentation in an Automatic Framework // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Miami, USA, 2009: 2036-2043
[7]
Larlus D, Verbeek J, Jurie F. Category Level Object Segmentation by Combining Bag-of-words Models with Dirichlet Processes and Random Fields. International Journal of Computer Vision, 2010, 88(2): 238-253
[8]
Fox E B, Sudderth E B, Jordan M I, et al. A Sticky HDP-HMM with Application to Speaker Diarization. The Annals of Applied Statistics, 2011, 5(2A): 1020-1056
[9]
Bosch A, Zisserman A, Muoz X. Scene Classification Using a Hybrid Generative Discriminative Approach. IEEE Trans on Pattern Analysis and Machine Intelligence, 2008, 30(4): 712-727
[10]
Holub A D, Welling M, Perona P. Hybrid Generative-Discriminative Visual Categorization. International Journal of Computer Vision, 2008, 77(1/2/3): 239-258
[11]
Fujino A, Ueda N, Saito K. Semisupervised Learning for a Hybrid Generative/ Discriminative Classifier Based on the Maximum Entropy Principle. IEEE Trans on Pattern Analysis and Machine Intelligence, 2008, 30(3): 424-437
[12]
Chapelle O, Sch lkopf B, Zien A. Semi-Supervised Learning. Cambridge, USA: MIT Press, 2006
[13]
Kalal Z, Mikolajczyk K, Matas J. Tracking-Learning-Detection. IEEE Trans on Pattern Analysis and Machine Intelligence, 2012, 34(7): 1409-1422
[14]
Gao J, Xie Z, Zhang J, et al. Image Semantic Analysis and Understanding: A Review. Pattern Recognition and Artificial Intelligence, 2010, 23(2): 191-202 (in Chinese)(高 隽,谢 昭,张 骏,等.图像语义分析与理解综述.模式识别与人工智能, 2010, 23(2): 191-202)
[15]
Xun G, Wang H F. The Development of Topic Models in Natural Language Processing. Chinese Journal of Computers, 2011, 34(8): 1423-1436 (in Chinese)(徐 戈,王厚峰.自然语言处理中主题模型的发展.计算机学报, 2011, 34(8): 1423-1436)
[16]
Sudderth E B, Torralba A, Freeman W T, et al. Describing Visual Scenes Using Transformed Objects and Parts. International Journal of Computer Vision, 2008, 77(1/2/3): 291-330
[17]
Sudderth E B, Torralba A, Freeman W T, et al. Learning Hierarchical Models of Scenes, Objects, and Parts // Proc of the 10th IEEE International Conference on Computer Vision. Beijing, China, 2005, II: 1331-1338
[18]
Blei D M. Introduction to Probabilistic Topic Models. Communications of the ACM, 2012, 55(4): 77-84
[19]
Lowe D G. Distinctive Image Features from Scale-invariant Keypoints. International Journal of Computer Vision, 2004, 60(2): 91-110
[20]
Oliva A, Torralba A. Building the Gist of a Scene: The Role of Global Image Features in Recognition. Progress in Brain Research, 2006, 155: 23-36
