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基于潜在Dirichlet分布的图像分层表示模型

, PP. 1146-1153

Keywords: 图像分层表示,前馈,概率模型,潜在Dirichlet分布(LDA)

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Abstract:

现有的图像分层表示方法严格局限于前馈型方式,不能较好地解决局部模糊性等问题。基于此,文中提出一种学习和推断层次结构所有分层的概率模型,它考虑递归的概率分解过程,通过推导得到金字塔式多层结构的潜在Dirichlet分布的衍生模型。该模型存在两个重要特性:增加表示层可提高平面模型的性能;采用全Bayesian概率方法优于其前馈型实现形式。在标准识别数据集上的实验结果表明,与现有的分层表示方法相比,该模型表现出较好性能。

References

[1]  Lowe D G. Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision, 2004, 60(2): 91-110
[2]  Ahmed A, Yu Kai, Xu Wei, et al. Training Hierarchical Feed-Forward Visual Recognition Models Using Transfer Learning from Pseudo-Tasks // Proc of the 10th European Conference on Computer Vision. Marseille, France, 2008: 69-82
[3]  Lazebnik S, Schmid C, Ponce J. Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. New York, USA, 2006: 2169-2178
[4]  Yang Jianchao, Yu Kai, Gong Yihong, et al. Linear Spatial Pyramid Matching Using Sparse Coding for Image Classification // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Miami, USA, 2009: 1794-1801
[5]  Olshausen B A, Field D J. Sparse Coding with an Over-Complete Basis Set: A Strategy Employed by V1? Vision Research, 1997, 37(23): 3311-3325
[6]  Boureau Y L, Bach F, LeCun Y, et al. Learning Mid-Level Features for Recognition // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. San Francisco, USA, 2010: 2559-2566
[7]  Fritz M, Black M J, Bradski G R, et al. An Additive Latent Feature Model for Transparent Object Recognition // Proc of the 23rd An-nual Conference on Neural Information Processing Systems. Vancouver, Canada, 2009: 558-566
[8]  Mutch J, Lowe D G. Object Class Recognition and Localization Using Sparse Features with Limited Receptive Fields. International Journal of Computer Vision, 2008, 80(1): 45-47
[9]  Rolls E, Deco G. Computational Neuroscience of Vision. Oxford, UK: Oxford University Press, 2002
[10]  Lee H, Grosse R, Ranganath R, et al. Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Repre-sentations // Proc of the 26th Annual International Conference on Machine Learning. Montreal, Canada, 2009: 609-616
[11]  Ranzato M A, Huang Fujie, Boureau Y L, et al. Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition[EB/OL].[2012-05-30].http://www.cs.nyu.edu/~ylan/files/publi/ranzato-cvpr-07.pdf
[12]  Serre T, Wolf L, Bileschi S, et al. Robust Object Recognition with Cortex-Like Mechanisms. IEEE Trans on Pattern Analysis and Machine Intelligence, 2007, 29(3): 411-426
[13]  Sivic J, Russell B C, Efros A A, et al. Discovering Objects and Their Locations in Images // Proc of the 10th IEEE International Conference on Computer Vision. Beijing, China, 2005, I: 370-377
[14]  Blei D M, Ng A Y, Jordan M I. Latent Dirichlet Allocation. Journal of Machine Learning Research, 2003, 3(1): 993-1022
[15]  Blei D M, Griffiths T L, Jordan M I, et al. Hierarchical Topic Models and the Nested Chinese Restaurant Process[EB/OL]. [2012-07-25]. http://machinelearning.wustl.edu/mlpapers/paper_files/NIPS2003_AA03.pdf
[16]  Ferguson T S. A Bayesian Analysis of Some Nonparametric Problems. The Annals of Statistics, 1973, 1(3): 209-230
[17]  Heinrich G. Parameter Estimation for Text Analysis[EB/OL]. [2012-07-25]. http://www.arbylon.net/publications/text-est.pdf
[18]  Li Feifei, Fergus R, Perona P. Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories. Computer Vision and Image Understanding, 2004, 106(1): 59-70
[19]  Kavukcuoglu K, Sermanet P, Boureau Y L, et al. Learning Convolutional Feature Hierarchies for Visual Recognition[EB/OL]. [2012-07-25]. http://yann.lecun.com/exdb/publis/pdf/koray-nips-10.pdf
[20]  Fidler S, Boben M, Leonardis A. Similarity-Based Cross-Layered Hierarchical Representation for Object Categorization[EB/OL].[2012-06-10].http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4587409&tag=1

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