Texture classification by multi-model feature integration using Bayesian networks

Pattern Recognition Letters

Volumn 24, Issue 1-3, 2003, Pages 393-401

  Huang, Yong ^a   Chan, Kap Luk ^a   Zhang, Zhihua ^a  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

Bayesian networks; Feature integration; Gabor filter; Gaussian Markov random field; Texture classification

Indexed keywords

CLASSIFICATION (OF INFORMATION); IMAGE ANALYSIS; MARKOV PROCESSES; MATHEMATICAL MODELS;

IMAGE PROPERTIES;

FEATURE EXTRACTION;

IMAGE CLASSIFICATION;

EID: 0037230873     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0167-8655(02)00263-5     Document Type: Article

References (18)

1. Benediktsson, J.A., Sveinsson, J.R., Swaine, P.H., 1997. Hybrid consensus theoretic classification. IEEE Trans. Geosci. Remote Sensing 35 (4), 833-843.
2. Brodatz, P., 1966. Textures: A Photographic Album for Artists and Designers, Dover.
3. Chen, C.C., Huang, C.L., 1993. Markov random fields for texture classification. Pattern Recogn. Lett. 14, 907-914.
4. Duda, R.O., Hart, P.E., Stork, D.G., 2000. Pattern Classification, second ed. John Wiley & Sons, New York.
5. Francos, J.M., Meiri, A.Z., Porat, B., 1993. A unified texture model based on a 2-D Wold-like decomposition. IEEE Trans. Signal Proces. 41 (8), 2665-2678.
6. Friedman, N., Geiger, D., Goldszmidt, M., 1997. Bayesian network classifiers. Mach. Learn. 29, 131-163.
7. Good, I.J., 1950. Probability and the Weighing of Evidence. Charles Griffin, London.
8. Heeger, D.J., Bergen, J.R., 1995. Pyramid-based texture analysis/synthesis. In: Proceedings of IEEE International Conference on Image Processing, Vol. 3, pp. 648-651.
9. Huang, Y., Chan, K.L., Huang, Z.Y., 2001. Harmonic extraction based on higher order statistics spectrum decomposition for a unified texture model. In: Proceedings of IEEE International Conference on Computer Vision, Vol. 2, pp. 245-250.
10. Jain, A.K., Farrokhnia, F., 1991. Unsupervised texture segmentation using Gabor filters. Pattern Recogn. 24 (12), 1167-1186.
11. Jensen, F.V., 2001. Bayesian networks and decision graphs. Springer-Verlag, Berlin.
12. Kashyap, R.L., Chellappa, R., 1983. Estimation and choice of neighbors in spatial interaction models of images. IEEE Trans. Inform. Theory IT-29, 60-72.
13. Liu, F., 1997. Modeling spatial and temporal textures. Ph.D. thesis, MIT.
14. Manjunath, B.S., Ma, W.Y., 1996. Texture features for browsing and retrieval of image data. IEEE Trans. Pattern Anal. Mach. Intell. 18 (8), 837-842.
15. Ng, L.S., Nixon, M.S., Carter, J.N., 1998. Texture classification using combined feature sets. In: IEEE Southwest Symposium on Image Analysis and Interpretation, pp. 103-108.
16. Pearl, J., 1988. Probabilistic reasoning in expert system: networks of plausible inference. Morgan Kaufmann, San Mateo, CA.
17. Wang, L., Liu, J., Li, S.Z., 1998. Texture classification using wavelet decomposition with Markov random field model. In: Proceedings 14th International Conference on Pattern Recognition, Vol. 2, pp. 1613-1615.
18. Zhu, S.C., Wu, Y., Mumford, D., 1996. FRAME: filters, random fields and minimax entropy-towards a unified theory for texture modeling. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, pp. 686-693.