A hierarchical clustering approach for image datasets

Humans analyze images mostly on their semantics. But such a semantic clustering of images is one of the difficult tasks in the field of computer vision. A clustering algorithm is proposed in this work to achieve a dataset with images grouped semantically. It does not utilize any background knowledge related either to the semantics of images or the number of clusters formed. The algorithm is based on the agglomerative method of hierarchical clustering algorithm. At each intermediate step, a representative image is chosen to denote a cluster. This image stands for every other image belonging to a cluster and hence there is some loss of information. This loss is tracked to get the number of clusters automatically. Experimental results on four datasets of varying sizes are presented which show the efficiency and effectiveness of the proposed algorithm. The results are also compared with a popular k-means algorithm.