This publication was produced by Christos Avgerinos, Vassilios Solachidis, Nicholas Vretos and Petros Daras of the NADINE technical partner, CERTH, in conjunction with, and as a result of the innovative technical work being carried out on the NADINE platform:

In this paper, a novel algorithm for non-parametric image clustering, is proposed. Non-parametric clustering methods operate by considering the number of clusters unknown as opposed to parametric clustering, where the number of clusters is known a priori. In the present work, a deep neural network is trained, in order to decide whether an arbitrary sized group of elements can be considered as a unique cluster or it consists of more than one clusters. Using this trained neural network as clustering criterion, an iterative algorithm is built, able to cluster any given dataset. Evaluation of the proposed method on several public datasets shows that the proposed method is either on par or outperforms state-of-the-art methods even when compared to parametric image clustering methods. The proposed method is additionally able to correctly cluster input samples from a completely different dataset than the one it has been trained on, as well as data coming from different modalities. Results on cross-dataset clustering show evidence of the generalization potential of the proposed method.

The full paper can be viewed here: