ABSTRACT
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.
- Jia-Yu Pan, Hyung-Jeong Yang, Pinar Duygulu, and Christos Faloutsos, "Automatic image captioning," in Proceedings of the 2004 IEEE International Conference on Multimedia and Expo (ICME 2004), 2004, pp. 1987--1990.Google Scholar
- Jonathon S. Hare and Paul H. Lewis, "Saliency-based models of image content and their application to auto-annotation by semantic propagation," in Proceedings of Multimedia and the Semantic Web / European Semantic Web Conference 2005, 2005.Google Scholar
- D. D. Lee and H. S. Seung, "Learning the parts of objects by non-negative matrix factorization," Nature, vol. 401, pp. 788, october 1999.Google ScholarCross Ref
- S. Tsuge, M. Shishibori, S. Kuroiwa, and K. Kita, "Dimensionality reduction using non-negative matrix factorization for information retrieval," in IEEE International Conference on Systems, Man, and Cybernetics, 2001, pp. 960--965.Google Scholar
- Wei Xu, Xin Liu, and Yihong Gong, "Document clustering based on non-negative matrix factorization," in SIGIR '03: Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval. 2003, pp. 267--273, ACM Press. Google ScholarDigital Library
- David Guillamet, Jordi Vitrià, and Bernt Schiele, "Introducing a weighted non-negative matrix factorization for image classification," Pattern Recognition Letters, vol. 24, no. 14, pp. 2447--2454, 2003. Google ScholarDigital Library
- Weixiang Liu and Nanning Zheng, "Non-negative matrix factorization based methods for object recognition," Pattern Recognition Letters, vol. 25, no. 8, pp. 893--897, 2004. Google ScholarDigital Library
- Daniel D. Lee and H. Sebastian Seung, "Algorithms for non-negative matrix factorization," in Advances in Neural Information Processing Systems, 2001, pp. 556--562.Google Scholar
- Patrik O. Hoyer, "Non-negative matrix factorization with sparseness constraints," The Journal of Machine Learning Research, vol. 5, pp. 1457--1469, 2004. Google ScholarDigital Library
- Eric Gaussier and Cyril Goutte, "Relation between plsa and nmf and implications," in Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, 2005, pp. 601 -- 602. Google ScholarDigital Library
- Chris Ding, Tao Li, and Wei Peng, "Nmf and plsi: equivalence and a hybrid algorithm," in Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, 2006, pp. 641 -- 642. Google ScholarDigital Library
- Jonathon S. Hare and Paul H. Lewis, "Salient regions for query by image content.," in CIVR '04: Proceedings of the 6th ACM international conference on Image and video retrieval, 2004, pp. 317--325.Google Scholar
- David G. Lowe, "Distinctive image features from scale-invariant keypoints," International Journal of Computer Vision, vol. 60, no. 2, pp. 91--110, 2004. Google ScholarDigital Library
- Chih-Jen Lin, "Projected gradient methods for non-negative matrix factorization," Tech. Rep. Information and Support Service ISSTECH-95-013, Department of Computer Science, National Taiwan University, 2005.Google Scholar
- Jianbo Shi and Jitendra Malik, "Normalized cuts and image segmentation," IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), pp. 888--905, 2000. Google ScholarDigital Library
- Bryan C. Russell, Alexei A. Efros, Josef Sivic, William T. Freeman, and Andrew Zisserman, "Using multiple segmentations to discover objects and their extent in image collections," in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, June 2006, pp. 1605--1614. Google ScholarDigital Library
- Scott Deerwester, Susan T. Dumais, George W. Furnas, Thomas K. Landauer, and Richard Harshman, "Indexing by latent semantic analysis," Journal of the American Society for Information Science, vol. 41, pp. 391 -- 407, 1990.Google ScholarCross Ref
- Kobus Barnard, Pinar Duygulu, Nando de Freitas, David Forsyth, David Blei, and Michael I. Jordan, "Matching words and pictures," Journal of Machine Learning Research, vol. 3, pp. 1107--1135, 2003. Google ScholarDigital Library
- Jonathon S. Hare, Paul H. Lewis, Peter G. B. Enser, and Christine J. Sandom, "A linear-algebraic technique with an application in semantic image retrieval," in CIVR '06: Proceedings of the 6th ACM international conference on Image and video retrieval, 2006, pp. 31--40. Google ScholarDigital Library
Index Terms
- Non-negative matrix factorisation for object class discovery and image auto-annotation
Recommendations
Deep alternating non-negative matrix factorisation
AbstractNon-negative matrix factorisation (NMF) is a promising data-mining technique for non-negative data. NMF achieves feature extraction by factorising the original data matrix into a basis matrix and coding matrix both with non-negative entries. ...
A multi-view-group non-negative matrix factorization approach for automatic image annotation
In automatic image annotation (AIA) different features describe images from different aspects or views. Part of information embedded in some views is common for all views, while other parts are individual and specific. In this paper, we present the Mvg-...
Discovering speech phones using convolutive non-negative matrix factorisation with a sparseness constraint
Discovering a representation that allows auditory data to be parsimoniously represented is useful for many machine learning and signal processing tasks. Such a representation can be constructed by non-negative matrix factorisation (NMF), a method for ...
Comments