Learning to combine bottom-up and top-down segmentation

International Journal of Computer Vision

Volumn 81, Issue 1, 2009, Pages 105-118

  Levin, Anat ^a   Weiss, Yair ^a  

^a HEBREW UNIVERSITY OF JERUSALEM   (Israel)

Author keywords

Image segmentation; Object detection

Indexed keywords

DIGITAL IMAGE STORAGE; IMAGE PROCESSING; IMAGE RETRIEVAL; IMAGE SEGMENTATION; OBJECT RECOGNITION; ROOM AND PILLAR MINING;

BOTTOM-UP; COARSE SEGMENTATIONS; CONDITIONAL RANDOM FIELDS; GROUND TRUTHS; HIGH QUALITIES; IMAGE INFORMATIONS; INDUCTION ALGORITHMS; LEARNING PROCEDURES; OBJECT DETECTION; SEGMENTATION ALGORITHMS; TOP DOWNS; TRAINING SETS;

LEARNING ALGORITHMS;

EID: 58049184640     PISSN: 09205691     EISSN: 15731405     Source Type: Journal    
DOI: 10.1007/s11263-008-0166-0     Document Type: Article

References (25)

1. Barbu, A., & Zhu, S. C. (2003). Graph partition by swendsen-wang cut. In Proceedings of the IEEE international conference on computer vision.
2. Borenstein, E., & Ullman, S. (2002). Class-specific, top-down segmentation. In Proc. of the European conf. on comput. vision, May 2002.
3. Borenstein, E., Sharon, E., & Ullman, S. (2004). Combining top-down and bottom-up segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition workshop on perceptual organization in computer vision, June 2004.
4. Fei-Fei, L., Fergus, R., & Perona, P. (2006). One-shot learning of object categories. IEEE Transactions on Pattern Analysis and Machine Intelligence.
5. He, X., Zemel, R., & Carreira-Perpinan, M. (2004). Multiscale conditional random fields for image labeling. In Proceedings of the IEEE conference on computer vision and pattern recognition.
6. He, X., & Zemel, R. S. Debajyoti, R., (2006). Learning and incorporating top-down cues in image segmentation. In ECCV.
7. Kumar, S., & Hebert, M. (2003). Discriminative random fields: A discriminative framework for contextual interaction in classification. In Proceedings of the IEEE international conference on computer vision.
8. Kumar, M. P., Torr, P. H. S., & Zisserman, A. (2004). Objcut. In Proceedings of the IEEE Conference on computer vision and pattern recognition.
9. Lafferty, J., McCallum, A., & Pereira, F. (2001). Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In Proc. 18th international conf. on machine learning (pp. 282-289). San Francisco: Morgan Kaufmann.
10. Lafferty, J., Zhu, X., & Liu, Y. (2004). Kernel conditional random fields: Representation and clique selection. In ICML.
11. LeCun, Y., & Huang, F. J. (2005). Loss functions for discriminative training of energy-based models. In Proc. of the 10th international workshop on artificial intelligence and statistics (AIStats'05).
12. Leibe, B., Leonardis, A., & Schiele, B. (2004). Combined object categorization and segmentation with an implicit shape model. In Proceedings of the workshop on statistical learning in computer vision, Prague, Czech Republic, May 2004.
13. Malik, J., Belongie, S., Leung, T., & Shi, J. (2000). Contour and texture analysis for image segmentation. In K. L. Boyer & S. Sarkar (Eds.), Perceptual organization for artificial vision systems. Dordrecht: Kluwer Academic.
14. McCallum, A. (2004). Efficiently inducing features of conditional random fields. In UAI.
15. Quattoni, A., Collins, M., & Darrell, T. (2004). Conditional random fields for object recognition. In NIPS.
16. Ren, X., Fowlkes, C., & Malik, J. (2005). Cue integration in figure/ground labeling. In Advances in neural information processing systems (NIPS).
17. Sharon, E., Brandt, A., & Basri, R. (2001). Segmentation and boundary detection using multiscale intensity measurements. In Proceedings of the IEEE conference on computer vision and pattern recognition.
18. Shotton, J., Winn, J., Rother, C., & Criminisi, A. (2006). Textonboost: Joint appearance, shape and context modeling for multi-class object recognition and segmentation. In ECCV.
19. Tu, Z. W., Chen, X. R., Yuille, A. L., & Zhu, S. C. (2003). Image parsing: segmentation, detection, and recognition. In Proceedings of the IEEE international conference on computer vision.
20. Wainwright, M. J., Jaakkola, T., & Willsky, A. S. (2003). Tree-reweighted belief propagation and approximate ml estimation by pseudo-moment matching. In 9th workshop on artificial intelligence and statistics.
21. Winn, J., & Jojic, N. (2005). Locus: Learning object classes with unsupervised segmentation. In Proc. int'l conf. comput. vision.
22. J. S. Yedidia W. T. Freeman Y. Weiss 2005 Constructing free-energy approximations and generalized belief propagation algorithms IEEE Transactions on Information Theory 51 2282 2312
23. Yu, S. X., & Shi, J. (2003). Object-specific figure-ground segregation. In Proceedings of the IEEE conference on computer vision and pattern recognition.
24. Yuille, A., & Hallinan, P. (2002). Deformable templates. In A. Blake & A. Yuille (Eds.), Active vision. Cambridge: MIT Press.
25. S. C. Zhu Z. N. Wu D. Mumford 1997 Minimax entropy principle and its application to texture modeling Neural Computation 9 8 1627 1660