Active semi-supervised fuzzy clustering

Clustering algorithms are increasingly employed for the categorization of image databases, in order to provide users with database overviews and make their access more effective. By including information provided by the user, the categorization process can produce results that come closer to user's expectations. To make such a semi-supervised categorization approach acceptable for the user, this information must be of a very simple nature and the amount of information the user is required to provide must be minimized. We propose here an effective semisupervised clustering algorithm, active fuzzy constrained clustering (AFCC), that minimizes a competitive agglomeration cost function with fuzzy terms corresponding to pairwise constraints provided by the user. In order to minimize the amount of constraints required, we define an active mechanism for the selection of candidate constraints. The comparisons performed on a simple benchmark and on a ground truth image database show that ...

• Type: Journal
• Year: 2008
• Where: PR
• Authors: Nizar Grira, Michel Crucianu, Nozha Boujemaa
• Keywords: Categorization, Image Databases, PR 2008, Semi-supervised Categorization Approach