Interpolation aided fuzzy image classification

Yongfeng Zhang, Changjing Shang, Qiang Shen

Abstract: 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.