A hierarchical classification system based on adaptive resonance theory

Date

2006-10-11

Author

UYSAL, Mutlu
Akbaş, Emre
Yarman Vural, Fatoş Tunay

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In this study, we propose a hierarchical classification system, which emulates the eye-brain channel in two hierarchical layers. In the first layer, a set of classifiers are trained by using low level, low dimensional features. In the second layer, the recognition results of the first layer are fed to the Fuzzy ARTMAP (FAM) classifier which implements the Adaptive Resonance Theory. Experiments indicate that the hierarchical approach proposed in this paper, increases the classification performances compared to the available methods.

Subject Keywords

Adaptive systems, Biological neural networks, Image analysis, Image color analysis , Resonance, Image classification, Shape measurement, MPEG 7 Standard, Fuzzy logic, Subspace constraints

URI

https://hdl.handle.net/11511/41160

DOI

https://doi.org/10.1109/icip.2006.313128

Collections

Department of Computer Engineering, Conference / Seminar

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Citation Formats

M. UYSAL, E. Akbaş, and F. T. Yarman Vural, “A hierarchical classification system based on adaptive resonance theory,” 2006, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/41160.