Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Document clustering based on non-negative matrix factorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Introducing a weighted non-negative matrix factorization for image classification
Pattern Recognition Letters
The Journal of Machine Learning Research
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Non-negative matrix factorization based methods for object recognition
Pattern Recognition Letters
Non-negative Matrix Factorization with Sparseness Constraints
The Journal of Machine Learning Research
Relation between PLSA and NMF and implications
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
NMF and PLSI: equivalence and a hybrid algorithm
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Using Multiple Segmentations to Discover Objects and their Extent in Image Collections
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
A linear-algebraic technique with an application in semantic image retrieval
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
Unsupervised Object Discovery: A Comparison
International Journal of Computer Vision
NMF-based multimodal image indexing for querying by visual example
Proceedings of the ACM International Conference on Image and Video Retrieval
Object-based visual query suggestion
Multimedia Tools and Applications
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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 non-negativity constraints which lead to its parts-based representation characteristic. In 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.