Density-Sorting-Based Convolutional Fuzzy Min-Max Neural Network for Image Classification

Abstract

Traditional image classification methods mostly use offline learning mode, which takes a lot of time when data is updated. In this paper, we propose a density-sorting-based convolutional fuzzy min-max neural network (DCFMNN) for image classification to solve this problem. DCFMNN is realized based on convolutional Neural Network (CNN) and density-sorting-based fuzzy min-max neural network. CNN is applied for image feature extraction. Density-sorting-based fuzzy min-max neural network is used for classification, which includes density-based sorting part and fuzzy min-max (FMM) neural network part. In the part of density-based sorting, patterns are sorted according to the points with the highest density in the same class and two densest points are considered for selection. The purpose is to overcome the influence of the pattern input order in the original FMM on the creation of the hyperbox. In the part of FMM, the fuzzy set classification method is used to enable online learning. Diverse CNN architectures are applied to DCFMNN. The benchmark image datasets were employed for evaluation on DCFMNN. Experimental results show that DCFMNN has high classification accuracy and less network complexity, and its online learning ability reduces the training time.