A semi-supervised probabilistic model for clustering large databases of complex images

Abstract

Image content clustering is an effective way to organize large databases thereby making the content based image retrieval process much easier. However, clustering of images with varied background and foreground is quite challenging. In this paper, we propose a novel image content clustering paradigm suitable for clustering large and diverse image databases. In our approach images are represented in a continuous domain based on a probabilistic Gaussian Mixture Model (GMM) with the images modeled as mixture of Gaussian distributions in the selected feature space. The distance metric between the Gaussian distributions is defined in the sense of Kullback–Leibler (KL) divergence. The clustering is done using a semi-supervised learning framework where labeled data in the form of cluster templates is used to classify the unlabelled data. The clusters are formed around initially chosen seeds and are updated in the due course based on user inputs. In our clustering approach the user interaction is done in a structured way as to get maximum inputs from the user in a limited time. We propose two methods to carry out the structured user interaction using which the cluster templates are updated to improve the quality of the clusters formed. The proposed method is experimentally evaluated on benchmark datasets that are specifically chosen to include a wide variation of images around a common theme that is typically encountered in applications like photo-summarization and poses a major semantic gap challenge to conventional clustering approaches. The experimental results presented demonstrate the effectiveness of the proposed approach.