A new interactive semi-supervised clustering model for large image database indexing

• Propose a new interactive semi-supervised clustering model.
• Present an experimental comparison between our model and the semi-supervised HMRF-kmeans.
• Present and compare strategies for deducing pairwise constraints from the user feedbacks.

Indexing methods play a very important role in finding information in large image databases. They organize indexed images in order to facilitate, accelerate and improve the results for later retrieval. Alternatively, clustering may be used for structuring the feature space so as to organize the dataset into groups of similar objects without prior knowledge (unsupervised clustering) or with a limited amount of prior knowledge (semi-supervised clustering).In this paper, we introduce a new interactive semi-supervised clustering model where prior information is integrated via pairwise constraints between images. The proposed method allows users to provide feedback in order to improve the clustering results according to their wishes. Different strategies for deducing pairwise constraints from user feedback were investigated. Our experiments on different image databases (Wang, PascalVoc2006, Caltech101) show that the proposed method outperforms semi-supervised HMRF-kmeans (Basu et al., 2004).