An observation-constrained generative approach for probabilistic classification of image regions

Image and Vision Computing

Volumn 21, Issue 1, 2003, Pages 87-97

  Kumar, Sanjiv ^a   Loui, Alexander C ^b   Hebert, Martial ^a  

^a CARNEGIE MELLON UNIVERSITY   (United States)

^b EASTMAN KODAK COMPANY   (United States)

Author keywords

Generative model; Image region classification; Image segmentation; Semantic interpretation

Indexed keywords

DATA REDUCTION; IMAGE ANALYSIS; MATHEMATICAL MODELS; PROBABILITY; SEMANTICS;

IMAGE REGIONS;

IMAGE SEGMENTATION;

EID: 0037428182     PISSN: 02628856     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0262-8856(02)00125-7     Document Type: Article

References (21)

1. Bishop C.M. Neural Networks for Pattern Recognition. 1995;Oxford University Press, New York.
2. Campbell N.W., Mackeown W.P.J., Thomas B.T., Troscianko T. The automatic classification of outdoor images. International Conference on Engineering Applications of Neural Networks. 1996;339-342.
3. C. Carson, S. Belongie, H. Grennspan, J. Malik, Region-based image querying, CVPR'97, Workshop on Content-based Access of Image and Video Libraries, 1997.
4. Dempster A., Laird N., Rubin D. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B. 39:1977;1-38.
5. C. Farley, A.E. Raftery, How many clusters? Which clustering method? Answers via model-based cluster analysis, Technical Report, No. 329, Department of Statistics, University of Washington, 1998.
6. Hansen M.H., Yu B. Model selection and the principle of minimum description length. JASA. 96:1998;746-774.
7. D. Heckerman, D. Chickering, A comparison of scientific and engineering criteria for Bayesian model selection, Technical Report MSR-TR-96-12, Microsoft Research, 1996.
8. Konishi S., Yuille A.L. Statistical cues for domain specific image segmentation with performance analysis. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition. 1:2000;125-132.
9. Kumar S., Loui A.C., Hebert M. Probabilistic classification of image regions using an observation-constrained generative approach. The ECCV Workshop on Generative Model Based Vision, Copenhagen. 2002;91-99.
10. Manjunath B.S., Ma W.Y. Texture features for browsing and retrieval of image data. IEEE Transactions on Pattern Analysis and Machine Intelligence. 18:1996;837-842.
11. Mao J., Jain A.K. Texture classification and segmentation using multiresolution simultaneous autoregressive models. Pattern Recognition. 25:1992;173-188.
12. Rikert T., Jones M., Viola P. A cluster-based statistical model for object detection. Proceedings of the IEEE International Conference on Computer Vision. 1999;1046-1053.
13. Rissanen J. Modeling by shortest data description. Automatica. 14:1978;465-471.
14. H. Schneiderman, A statistical approach to 3D object detection applied to faces and cars, PhD Thesis, Carnegie Mellon University, 2000.
15. Schneiderman H., Kanade T. A statistical method for 3D object detection applied to faces and cars. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2000;746-751.
16. Sochen N., Kimmel R., Malladi R. A general framework for low level vision. IEEE Transactions on Image Processing. 7:1998;310-318.
17. Storring M., Andersen H., Granum E. Skin colour detection under changing lighting condition. Araujo H., Dias J. Seventh Symposium on Intelligent Robotics Systems. 1999;187-195.
18. Strat T.M. Natural Object Recognition. 1992;Springer, Berlin.
19. Tanner M.A. Tools for Statistical Inference. Third ed: 1996;Springer, Berlin.
20. Torralba A., Sinha P. Statistical context priming for object detection. Proceedings of the International Conference on Computer Vision, ICCV'01. 2001.
21. A. Vailaya, Semantic classification in image databases, PhD thesis, Michigan State University, 2000.