Unsupervised multilayer fuzzy neural networks for image clustering

Abstract

Currently, labelling a large number of images is still a very challenging task. To tackle the problem of unlabelled data, unsupervised learning has been proposed. Among many unsupervised learning algorithms, K-means is the most popular algorithm. However, in a low-dimensional space, fuzzy c-means, which is more robust and less sensitive to initialization, has several advantages over K-means clustering. On the other hand, stacked convolutional pooling structures and manifold representation play pivotal roles in image clustering. In this paper, we propose an unsupervised multilayer fuzzy neural network for image clustering that unifies fuzzy systems, multilayer convolutional structures and manifold representation. The main contributions are as follows. First, we extend fuzzy systems to unsupervised tasks by introducing manifold representation, which expands the applications of fuzzy systems. Next, we propose the idea of using only a small number of attributes to compute firing strengths. This is implemented to prevent the firing strengths from falling to zero. Finally, randomly generated convolutional weights are used to extract features, which is a good choice for data without labels. It is demonstrated on a wide range of image datasets that the proposed approach is competitive with existing fuzzy and nonfuzzy clustering algorithms.