Interpolation aided fuzzy image classification

Yongfeng Zhang, Changjing Shang, Qiang Shen

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)


This paper presents a novel application of interpolation in supporting fuzzy image classification. The recently introduced Deep Spatio-Temporal Inference Network (DeSTIN) is employed to carry out limited original feature extraction. A simple but effective linear interpolation is then used to artificially increase the dimensionality of the extracted feature sets for accurate classification, without incurring heavy computational cost. In particular, Fuzzy-Rough Nearest Neighbour (FRNN) and Fuzzy Ownership Nearest Neighbour (FRNN-O) are each utilised for image classification. The work is tested against the popular MNIST dataset of handwritten digits [1]. Experimental results indicate that the proposed approach is highly promising.
Original languageEnglish
Title of host publicationIEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
PublisherIEEE Press
Publication statusPublished - 2015


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