A Novel Fuzzy Feature Encoding Approach for Image Classification

Date

2016-07-29

Author

Altintakan, Umit L.
Yazıcı, Adnan

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Feature encoding is a crucial step in BOW image representation. The standard BOW model assigns each image feature to the nearest visual-word without making a distinction between the features that are assigned to the same words. This hard feature assignment leads to high quantization errors and degrades the learning capacity of the classifiers in image classification. We propose a fuzzy feature encoding approach to overcome the uncertainty problem in BOW through assigning each image feature to the visual-words with some membership degrees. We employ two classification techniques, Naive Bayesian and SVM, to evaluate the effect of the fuzzy assignment in image classification. Experiments conducted on image datasets show that fuzzy feature encoding significantly improves the classification accuracy.

URI

https://hdl.handle.net/11511/55883

Conference Name

IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) held as part of IEEE World Congress on Computational Intelligence (IEEE WCCI)

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Department of Computer Engineering, Conference / Seminar

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Citation Formats

U. L. Altintakan and A. Yazıcı, “A Novel Fuzzy Feature Encoding Approach for Image Classification,” presented at the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) held as part of IEEE World Congress on Computational Intelligence (IEEE WCCI), Vancouver, CANADA, 2016, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/55883.