Classification and Retrieval of Images from Databases Using Rough Set Theory - Distributed Artificial Intelligence, Agent Technology, and Collaborative Applications - page 198

Yang, Z., & Laaksonen, J. (2005). Partial relevance in interactive facial image retrieval. In Proceedings
of 3rd International Conference on Advances in Pattern Recognition (ICAPR 2005), (pp. 216-225).

Yang , C. C., Prasher, S. O., Enright P., Madramootoo C., Burgess M. Goel, Pradeep K. & Callum I.
(2003, June) Application of decision tree technology for image classification using remote sensing data.
Agricultural Systems, 76 (3), 1101-1117.

Yining, D., & Manjuncth, B. S. (1999). An efficient low-dimensional color indexing scheme for region-
based image retrieval. In Proceedgins Of IEEE International. Conference on Acoustics, Speech, and
Signal Processing (ICASSP) , (pp. 3017-3020).

Zhang, H., Gong, Y., Low, C. Y., & Smoliar, S. W. (1995). Image retrieval based on color feature: An
evaluation study. In Proceedings Of SPIE, 2606 , (pp. 212-220).

Zhong, N., & Skowron, A. (2000). Rough sets in KDD: Tutorial notes. Bulletin of International Rough
Set Society, 4 (1/2), 9-30.

Zhou, Z. H., Jiang, Y., & Chen, S. F. (2000). A general neural framework for classification rule mining.
International Journal of Computers, Systems, and Signals, 1 (2), 154-168.

endnote

ROSETTA is a general C++ rough sets toolkit offering the most common rough sets struc-
ture and operations.

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