A Novel Fuzzy Visual Object Classification Approach

Date

2012-06-15

Author

Altintakan, Umit Lutfu
Yazıcı, Adnan
KOYUNCU, Murat

Metadata

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Support Vector Machines (SVMs) have been extensively used for visual object classification to bridge the semantic gap between the low level features and high level concepts. SVM treats each training input equally during the construction of its decision surface which results in poor learning machines if training data include outliers. In this paper, a novel fuzzy visual object classification approach utilizing Self-Organizing Maps (SOMs) in SVM is proposed. The experimental results show the effectiveness of the proposed Fuzzy SVM compared to the traditional SVM.

Subject Keywords

Fuzzy suppor vector machines, Membership function, Image classification, Self-organizing maps

URI

https://hdl.handle.net/11511/52893

Conference Name

IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

Collections

Department of Computer Engineering, Conference / Seminar

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Citation Formats

U. L. Altintakan, A. Yazıcı, and M. KOYUNCU, “A Novel Fuzzy Visual Object Classification Approach,” presented at the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Brisbane, QLD, Australia, 2012, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/52893.