Dynamic topology and relevance learning SOM-based algorithm for image clustering tasks

Abstract

In this paper, the task of unsupervised visual object categorization (UVOC) is addressed. We utilize a variant of Self-organizing Map (SOM) to cluster images in two different scenarios: disjoint (images from Caltech256) and non-disjoint (images from MSRC2) sets. First, we ran several tests to evaluate different image representation techniques: features obtained by a deep convolutional network were compared with those obtained by handcrafted methods, such as SIFT combined with a set of interest point detectors. As expected, we found that deep convolutional network features significantly outperformed its handcrafted counterparts. After choosing the best image representation technique, we compared the state-of-the-art image clustering algorithms with a SOM-based subspace clustering method that identifies automatically the relevant features in the high-dimensional image representations. The results have shown that our method achieves substantially lower clustering error than all competitors in several challenging testing settings.