Information Technology Reference
In-Depth Information
techniques in application area of multimedia processing, in particular image classification and retrieval
has become one of the most important ways of research of intelligent information processing. Neural
network shows us its strong ability to solve complex problems for many multimedia processing. From
the perspective of the specific rough sets approaches that need to be applied, explorations into pos-
sible applications of hybridize rough sets with other intelligent systems like neural networks, genetic
algorithms, fuzzy approaches, etc., to multimedia processing and content-based information system, in
particulars in multimedia computing problems could lead to new and interesting avenues of research
and it is always a challenge for the CI researchers.
note
This work was supported by Kuwait university, Research Grant No.[IQ01/03].
r eferences
Ahuja, N., & Rosefeld, A. (1978). A note on the use of second-order gray-level statistics for threshold
selection. IEEE Trans. Systems, Man, and Cybernatics, SMC (8) , 895-898.
Ashish G., Saroj, K., Meher, & Uma, B. S. (2008). A novel fuzzy classifier based on product aggregation
operator. Pattern Recognition, 41 (3), 961-971.
Bazan, J., Skowron, A., & Synak, P. (1994). Dynamic reducts as a tool for extracting laws from deci-
sion tables. In Proceedings of the Symposium On Methodologies for Intelligent Systems (pp. 346-355).
Berlin: Springer-Verlag.
Carson, C., Thomas, M., Belongie, S., Hellerstein, J. M., & Malik, J. (1999). Blobworld: A system for
region-based image indexing and retrieval. Third International Conference on Visual Information
Systems , Amsterdam, Netherland, pp. 509-516.
Cios, K., Pedrycz, W., & Swiniarski, R. (1998). Data mining methods for knowledge discovery. City:
Norwell, MA, USA, Kluwer Academic Publishers.
Graham, M. E. (2004). Enhancing visual resources for searching and retrieval - Is content based image
retrieval solution? Literary and Linguistic Computing, 19 (3), 321-333.
Dominik, S., Marcin, S., Szczuka & Jakub, W. (2004) Harnessing classifier networks: Towards hierar-
chical concept construction. Rough Sets and Current Trends in Computing 2004, (pp. 554-560).
Grzymala-Busse, J., Pawlak, Z., Slowinski, R., & Ziarko, W. (1999). Rough Sets. Communications of
the ACM, 38 (11), 88-95.
Haralick, R. M. (1979). Statistical and structural approaches to texture. Proceeding of the IEEE, 67 (5),
786-804.
Hassanien, A., & Ali, J. (2004). Enhanced rough sets rule reduction algorithm for classification digital
mammography. Intelligent System Journal, 13 (2), 117-151.
Search WWH ::




Custom Search