Modelling of spatial causality among distinctive properties of an image using conditional random field for image classification

References

• Boutell M, Brown CM, Luo J. Exploiting context for semantic scene classification. Univ. Rochester, Rochester, NY, Tech. Rep. 2006; 894. Available from: ftp://ftp.cs.rochester.edu/pub/papers/robotics/06.tr894.Exploiting_context_for_semantic_scene_classification.pdf
   Google Scholar
• Xiang T, Gong S. Activity based surveillance video content modelling. Pattern Recogn. 2008;41(7):2309–2326. doi: 10.1016/j.patcog.2007.11.024
   Web of Science ®Google Scholar
• Fei-Fei L, Perona P. A bayesian hierarchical model for learning natural scene categories. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05). IEEE; 2005.
   Google Scholar
• >Van Gemert, JC, Geusebroek, J-M, Veenman, CJ, et al. Kernel codebooks for scene categorization. European Conference on Computer Vision. Berlin: Springer; 2008. p. 696–709.
   Google Scholar
• Bosch A, Zisserman A, Muñoz X. Scene classification via pLSA. European Conference on Computer Vision. Springer; 2006.
   Google Scholar
• Luo J, Boutell M, Gray RT, et al. Image transform bootstrapping and its applications to semantic scene classification. IEEE Trans Syst Man Cybernet, Part B (Cybernetics). 2005;35(3):563–570.
   Google Scholar
• Yang, J, Jiang, Y-G, Hauptmann, AG, et al. Evaluating bag-of-visual-words representations in scene classification. Proceedings of the International Workshop on Workshop on Multimedia Information Retrieval. ACM; 2007. p. 197–206.
   Google Scholar
• Liu S, Bai X. Discriminative features for image classification and retrieval. Pattern Recogn Lett. 2012;33(6):744–751. doi: 10.1016/j.patrec.2011.12.008
   Web of Science ®Google Scholar
• Lazebnik S, Schmid C, Ponce J. Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06). IEEE; 2006.
   Google Scholar
• Bolovinou A, Pratikakis I, Perantonis S. Bag of spatio-visual words for context inference in scene classification. Pattern Recogn. 2013;46(3):1039–1053. doi: 10.1016/j.patcog.2012.07.024
   Web of Science ®Google Scholar
• Zhou L, Zhou Z, Hu D. Scene classification using a multi-resolution bag-of-features model. Pattern Recogn. 2013;46(1):424–433. doi: 10.1016/j.patcog.2012.07.017
   Web of Science ®Google Scholar
• >Xu, C, Wang, T, Gao, J, et al. An ordered-patch-based image classification approach on the image Grassmannian manifold. IEEE Trans Neur Net Learn Syst. 2014;25(4):728–737. doi: 10.1109/TNNLS.2013.2280752
   PubMed Web of Science ®Google Scholar
• >Artyomov, E, Rivenson, Y, Levi, G, et al. Morton (Z) scan based real-time variable resolution CMOS image sensor. IEEE Trans Circuit Syst Video Technol. 2005;15(7):947–952. doi: 10.1109/TCSVT.2005.848338
   Web of Science ®Google Scholar
• Hilbert D. Über die stetige Abbildung einer Linie auf ein Flächenstück. In Dritter Band: Analysis. Grundlagen der Mathematik Physik Verschiedenes. Springer; 1935. p. 1–2.
   Google Scholar
• Lafferty J, McCallum A, Pereira F. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. Proceedings of the Eighteenth International Conference on Machine Learning. ICML; 2001.
   Google Scholar
• Dixit M, Rasiwasia N., Vasconcelos N. Adapted Gaussian models for image classification. 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE; 2011.
   Google Scholar
• Reynolds D. Gaussian mixture models. In: Li SZ, editor. Encyclopedia of biometrics. New York: Springer; 2015. p. 827–832.
   Google Scholar
• Moon TK. The expectation–maximization algorithm. IEEE Signal Process Mag. 1996;13(6):47–60. doi: 10.1109/79.543975
   Web of Science ®Google Scholar
• Kullback S, Leibler RA. On information and sufficiency. Ann Math Stat. 1951;22(1):79–86. doi: 10.1214/aoms/1177729694
   Google Scholar
• Hershey JR, Olsen PA. Approximating the Kullback Leibler divergence between Gaussian mixture models. 2007 IEEE International Conference on Acoustics, Speech and Signal Processing – ICASSP'07. IEEE; 2007.
   Google Scholar
• Oliva A, Torralba A. Building the gist of a scene: the role of global image features in recognition. Prog Brain Res. 2006;155:23–36. doi: 10.1016/S0079-6123(06)55002-2
   PubMed Web of Science ®Google Scholar
• Chang C-C, Lin C-J. LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol. 2011;2(3):27. Available from: http://www.csie.ntu.edu.tw/∼cjlin/papers/libsvm.pdf doi: 10.1145/1961189.1961199
   Web of Science ®Google Scholar
• Griffin G, Holub A, Perona P. Caltech-256 object category dataset. Technical Report 7694, California Institute of Technology; 2007. Available from: http://authors.library.caltech.edu/7694
   Google Scholar
• Lazebnik S, Schmid C, Ponce J. A sparse texture representation using local affine regions. IEEE Trans Pattern Anal Mach Intell. 2005;27(8):1265–1278. doi: 10.1109/TPAMI.2005.151
   PubMed Web of Science ®Google Scholar
• Song T, Li H. WaveLBP based hierarchical features for image classification. Pattern Recogn Lett. 2013;34(12):1323–1328. doi: 10.1016/j.patrec.2013.04.020
   Web of Science ®Google Scholar
• Samaria FS, Harter AC. Parameterisation of a stochastic model for human face identification. Proceedings of the Second IEEE Workshop on Applications of Computer Vision. IEEE; 1994.
   Google Scholar
• Lee K-C, Ho J, Kriegman DJ. Acquiring linear subspaces for face recognition under variable lighting. IEEE Trans Pattern Anal Mach Intell. 2005;27(5):684–698. doi: 10.1109/TPAMI.2005.92
   PubMed Web of Science ®Google Scholar
• Wright J, Hua G. Implicit elastic matching with random projections for pose-variant face recognition. CVPR 2009: IEEE Conference on Computer Vision and Pattern Recognition. IEEE; 2009.
   Google Scholar
• Guojun L, Mei X. Face Recognition based on Riemannian Manifold Learning. 2011 International Conference on Computational Problem-Solving (ICCP). IEEE; 2011.
   Google Scholar