Non-negative matrix factorisation for object class discovery and image auto-annotation

Jiayu Tang, Paul H. Lewis
2008 Proceedings of the 2008 international conference on Content-based image and video retrieval - CIVR '08  
In information retrieval, sub-space techniques are usually used to reveal the latent semantic structure of a data-set by projecting it to a low dimensional space. Non-negative matrix factorisation (NMF), which generates a non-negative representation of data through matrix decomposition, is one such technique. It is different from other similar techniques, such as singular vector decomposition (SVD), in its nonnegativity constraints which lead to its parts-based representation characteristic. In
more » ... this paper, we present the novel use of NMF in two tasks; object class detection and automatic annotation of images. Experimental results imply that NMF is a promising sub-space technique for discovering the latent structure of image data-sets, with the ability of encoding the latent topics that correspond to object classes in the basis vectors generated.
doi:10.1145/1386352.1386370 dblp:conf/civr/TangL08 fatcat:hglozvxehje2zaeg5g5umzx4ye