[1]
|
Zhou Z H, Zhang M L. Multi-instance multi-label learning with application to scene classification. Advances in Neural Information Processing Systems. Cambridge, USA: MIT Press, 2006. 1609-1616
|
[2]
|
Makadia A, Pavlovic V, Kumar S. A new baseline for image annotation. In: Proceedings of the 10th European Conference on Computer Vision. Berlin, Heidelberg: Springer-Verlag, 2008, 5304: 316-329
|
[3]
|
Grauman K, Darell T. The pyramid match kernel: discriminative classification with sets of image features. In: Proceedings of the 10th International Conference on Computer Vision. Beijing, China: IEEE, 2005. 1458-1465
|
[4]
|
Lazebnik S, Schmid C, Ponce J. Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: Proceedings of the 2006 IEEE Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE, 2006. 2169-2178
|
[5]
|
Lowe D G. Towards a computational model for object recognition in IT cortex. In: Proceedings of the 1st IEEE International Workshop on Biologically Motivated Computer Vision. London, UK: Springer-Verlag, 2000. 20-31
|
[6]
|
Yang J C, Yu K, Gong Y H, Huang T. Linear spatial pyramid matching using sparse coding for image classification. In: Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition. Miami, USA: IEEE, 2009. 1794-1801
|
[7]
|
Yang D, Guo P. Image modeling with combined optimization techniques for image semantic annotation. Neural Computing & Applications, 2011, 20(7): 1001-1015
|
[8]
|
Zhou X, Cui N, Li Z, Liang F, Huang T S. Hierarchical gaussianization for image classification. In: Proceedings of the 12th IEEE International Conference on Computer Vision. Miami, USA: IEEE, 2009. 1971-1977
|
[9]
|
Tariq U, Lin K H, Li Z, Zhou X, Wang Z W, Le V, Huang T S, Lv X T, Han T X. Emotion recognition from an ensemble of features. In: Proceedings of the 2011 IEEE International Conference on Automatic Face & Gesture Recognition and Workshops. Santa Barbara, CA: IEEE, 2011. 872-877
|
[10]
|
Krapac J, Verbeek J, Jurie F. Spatial Fisher Vectors for Image Categorization, Technical Report INRIA-00613572, Institut National de Recherche en Informatique et en Automatique, France, 2011
|
[11]
|
Dixit M, Rasiwasia N, Vasconcelos N. Adapted Gaussian models for image classification. In: Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition. Washington DC, USA: IEEE, 2011. 937-943
|
[12]
|
Reynolds D A, Quatieri T F, Dunn R B. Speaker verification using adapted Gaussian mixture models. Digital Signal Processing, 2000, 10(1-3): 19-41
|
[13]
|
Wang C H, Yan S C, Zhang L, Zhang H J. Multi-label sparse coding for automatic image annotation. In: Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition. Miami, USA: IEEE, 2009. 1643-1650
|
[14]
|
Povey D, Chu S M, Varadarajan B. Universal background model based speech recognition. In: Proceedings of the 2008 IEEE International Conference on Acoustics, Speech and Signal Processing. Las Vegas, NV: IEEE, 2008. 4561-4564
|
[15]
|
Morrison G S. A comparison of procedures for the calculation of forensic likelihood ratios from acoustic-phonetic data: multivariate kernel density versus Gaussian mixture model-universal background model. Speech Communication, 2011, 53(2): 242-256
|
[16]
|
Bishop C M. Pattern Recognition and Machine Learning. New York: Springer-Verlag, 2006
|
[17]
|
Yang D, Guo P. Improvement of image modeling with affinity propagation algorithm for semantic image annotation. In: Proceedings of the 16th International Conference on Neural Information Processing. Berlin, Heidelberg: Springer-Verlag, 2009. 778-787
|
[18]
|
Duygulu P, Barnard K, de Freitas J F G, Forsyth D A. Object recognition as machine translation: learning a lexicon for a fixed image vocabulary. In: Proceedings of the 7th European Conference on Computer Vision. London, UK: Springer, 2002. 97-112
|