A novel optimized image feature selection algorithm using pairwise classifiers

Mahdi Bazarganigilani, Ben Arko, Ali Syed, Camille Price, Sandid Burki, Belinda Fridey, Michael Baron

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Abstract

This paper introduces an optimized method to improve the accuracy of content based image retrieval systems (CBIR). CBIR systems classify the images according to low and higher features. This research improves both feature selection and classifier partition of a CBIR system. This paper normalizes and descretizes the feature vectors. A pair-wise classifier is employed to improve the accuracy of classification. Results show high accuracy of proposed classifier in an image database.
Original languageEnglish
Pages (from-to)107-116
Number of pages10
JournalInternational Journal of Multimedia and Ubiquitous Engineering
Volume8
Issue number1
Publication statusPublished - Jan 2013

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    Bazarganigilani, M., Arko, B., Syed, A., Price, C., Burki, S., Fridey, B., & Baron, M. (2013). A novel optimized image feature selection algorithm using pairwise classifiers. International Journal of Multimedia and Ubiquitous Engineering, 8(1), 107-116.