Located hidden random fields: Learning discriminative parts for object detection

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3953 LNCS, Issue , 2006, Pages 302-315

  Kapoor, Ashish ^a   Winn, John ^b  

^a MIT MEDIA LAB   (United States)

^b MICROSOFT RESEARCH   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

DATABASE SYSTEMS; IMAGE SEGMENTATION; LEARNING SYSTEMS; MATHEMATICAL MODELS; PRINCIPAL COMPONENT ANALYSIS; RANDOM PROCESSES;

LOCATED HIDDEN RANDOM FIELD (LHRF); PART-BASED DETECTION; STATE-OF-THE-ART DETECTION; TRAINING SETS;

OBJECT RECOGNITION;

EID: 33745813565     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11744078_24     Document Type: Conference Paper

References (17)

1. Lafferty, J., McCallum, A., Pereira, F.: Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: International Conference on Machine Learning. (2001)
2. Crandall, D., Felzenszwalb, P., Huttenlocher, D.: Spatial priors for part-based recognition using statistical models. In: CVPR. (2005)
3. Agarwal, S., Roth, D.: Learning a sparse representation for object detection. In: European Conference on Computer Vision. (2002)
4. Leibe, B., Leonardis, A., Schiele, B.: Combined object categorization and segmentation with an implicit shape model. In: Workshop on Statistical Learning in Computer Vision. (2004)
5. Fergus, R., Perona, P., Zisserman, A.: Object class recognition by unsupervised scale-invariant learning. In: Computer Vision and Pattern Recognition. (2003)
6. Fergus, R., Perona, P., Zisserman, A.: A sparse object category model for efficient learning and exhaustive recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Diego. (2005)
7. Winn, J., Jojic, N.: LOCUS: Learning Object Classes with Unsupervised Segmentation, In: International Conference on Computer Vision. (2005)
8. Borenstein, E., Sharon, E., Ullman, S.: Combining top-down and bottom-up segmentation. In: Proceedings IEEE workshop on Perceptual Organization in Computer Vision, CVPR 2004. (2004)
9. Shotton, J., Blake, A., Cipolla, R.: Contour-based learning for object detection. In: International Conference on Computer Vision. (2005)
10. Kumar, S., Hebert, M,: Discriminative random fields: A discriminative framework for contextual interaction in classification. In: ICCV, (2003)
11. Quattoni, A., Collins, M., Darrell, T.: Conditional random fields for object recognition. In: Neural Information Processing Systems. (2004)
12. Kumar, M.P., Torr, P.H.S., Zisserman, A.: OBJ CUT. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Diego. (2005)
13. Szummer, M.: Learning diagram parts with hidden random fields. In: International Conference on Document Analysis and Recognition. (2005)
14. Kolmogorov, V.: Convergent tree-reweighted message passing for energy minimization. In: Workshop on Artificial Intelligence and Statistics. (2005)
15. Felzenszwalb, P., Huttenlocher, D.: Efficient belief propagation for early vision. In: Computer Vision and Pattern Recognition. (2004)
16. Lowe, D.: Object recognition from local scale-invariant features. In: International Conference on Computer Vision. (1999)
17. Garg, A., Agarwal, S., Huang., T.S.: Fusion of global and local information for object detection. In: International Conference on Pattern Recognition. (2002)