An Improved BOW Approach Using Fuzzy Feature Encoding and Visual-word Weighting

Date

2015-08-05

Author

Altintakan, Umit L.
Yazıcı, Adnan

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The bag-of-words (BOW) has become a popular image representation model with successful implementations in visual analysis. Although the original model has been improved in several ways, the utilization of the Fuzzy Set Theory in BOW has not been investigated thoroughly. This paper presents a fuzzy feature encoding approach to address the problems associated with the hard and soft assignments of image features to the visualwords. Our encoding method assigns each image feature to only the first and second closest words in the codebook to overcome the word-uncertainty problem. Moreover, we introduce a new word-weighting scheme for image categories based on image histograms. Experiments conducted on some image datasets show that both methods increase the BOW performance in content based image retrieval.

URI

https://hdl.handle.net/11511/55629

Conference Name

IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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Department of Computer Engineering, Conference / Seminar

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Citation Formats

U. L. Altintakan and A. Yazıcı, “An Improved BOW Approach Using Fuzzy Feature Encoding and Visual-word Weighting,” presented at the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Istanbul, TURKEY, 2015, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/55629.