Reconfigurable models for scene recognition

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2012, Pages 2775-2782

  Parizi, Sobhan Naderi ^a   Oberlin, John G ^a   Felzenszwalb, Pedro F ^a  

^a Brown University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BAG OF WORDS; DISCRIMINATIVE METHODS; DISCRIMINATIVE TRAINING; ESTIMATE MODEL; EXPECTATION-MAXIMIZATION ALGORITHMS; IMAGE REGIONS; LATENT VARIABLE; LATENT VARIABLE MODELS; RECONFIGURABLE MODELS; SCENE RECOGNITION;

ALGORITHMS;

COMPUTER VISION;

EID: 84866674135     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2012.6248001     Document Type: Conference Paper

References (17)

1. C. Bishop. Pattern Recognition and Machine Learning. Springer, 2006. 2, 4
2. A. Bosch, A. Zisserman, and X. Munoz. Scene classification using a hybrid generative/discriminative approach. TPAMI, 2008. 2
3. L. Fei-Fei and P. Perona. A bayesian hierarchical model for learning natural scene categories. In CVPR, 2005. 2
4. P. Felzenszwalb, R. Girshick, D. McAllester, and D. Ramanan. Object detection with discriminatively trained part based models. TPAMI, 2010. 3, 6
5. S. Lazebnik, C. Schmid, and J. Ponce. Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In CVPR, 2006. 1, 4, 6
6. P. Lipson, W. Grimson, and P. Sinha. Configuration based scene classification and image indexing. In CVPR, 1997. 2
7. D. Lowe. Distinctive image features from scale-invariant keypoints. IJCV, 2004. 6
8. A. Oliva and A. Torralba. Modeling the shape of the scene: a holistic representation of the spatial envelope. IJCV, 2001. 6
9. M. Pandey and S. Lazebnik. Scene recognition and weakly supervised object localization with deformable part-based models. In ICCV, 2011. 2, 6, 8
10. A. Quattoni and A. Torralba. Recognizing indoor scenes. In CVPR, 2009. 2, 6
11. I. Tsochantaridis, T. Joachims, T. Hofmann, and Y. Altun. Large margin methods for structured and interdependent output variables. JMLR, 2006. 3
12. J. Wu and J. Rehg. Centrist: A visual descriptor for scene categorization. TPAMI, 2010. 6
13. E. Xing, L. Li, H. Su, and L. Fei-Fei. Object bank: A high level image representation for scene classification and semantic feature sparsification. In NIPS, 2010. 6
14. O. Yakhnenko, J. Verbeek, and C. Schmid. Region-based image classification with a latent svm model. INRIA Tech. Rep. 7665, 2011. 2
15. C.-N. J. Yu and T. Joachims. Learning structural svms with latent variables. In ICML, 2009. 2, 3, 5
16. A. Yuille and A. Rangarajan. The concave-convex procedure. Neural Computation, 2003. 3, 5
17. J. Zhu, L. Li, L. Fei-Fei, and E. Xing. Large margin training of upstream scene understanding models. In NIPS, 2010. 6