Image clustering solely based on visual features without any knowledge or background information suffers from the problem of semantic gap. In this paper, we propose SS-NMF: a semi-supervised non-negative matrix factorization framework for image clustering. Accumulated relevance feedback in a CBIR system is treated as user provided supervision for guiding the image clustering. We consider the set of positive images in the feedback as constraints on the clustering specifying that the images "œmust" be clustered together. Similarly, negative images provide constraints specifying that they "œcannot" be clustered along with the positive images. Through an iterative algorithm, we perform symmetric trifactorization of the image-image similarity matrix to infer the clustering. Theoretically, we prove the correctness of SS-NMF by showing that the algorithm is guaranteed to converge. Through experiments conducted on general purpose image datasets, we demonstrate the superior performance of SS-NMF for clustering images effectively.

Date of creation, presentation, or exhibit



© ACM, 2009. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in the Proceedings of ACM Multimedia 2007.

Document Type

Conference Proceeding

Department, Program, or Center

Computer Science (GCCIS)


RIT – Main Campus