Unsupervised Fuzzy Neural Network for Image Clustering

Fuzzy systems have proven to be an effective tool for classification and regression. However, they have been mainly applied to supervised tasks. In this paper, we extend fuzzy systems to tackle unsupervised problems based on the manifold regularization framework and convolution/pooling technologies. The proposed fuzzy system, referred to as the unsupervised fuzzy neural network, can extract features from raw images accurately and perform well on image clustering. The main structure of the proposed approach is divided into three parts: fuzzy mapping, unsupervised feature extraction and manifold representation. We adopt K-means to perform clustering in the low-dimensional manifold space. Experimental results on image datasets demonstrate that our approach is competitive with classical and state-of-the-art algorithms. We also identify the relative contributions of each component of the proposed approach in experiments.