Abstract

A novel latent semantic indexing (LSI) approach for content-based image retrieval is presented in this paper. Firstly, an extension of non-negative matrix factorization (NMF) to supervised initialization is discussed. Then, supervised NMF is used in LSI to find the relationships between low-level features and high-level semantics. The retrieved results are compared with other approaches and a good performance is obtained.

© 2006 Chinese Optics Letters

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References

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  1. Z. Pecenovic, Image Retrieval Using Latent Semantic Indexing M. Sc. Thesis (Swiss Federal Institute of Technology, Lausanne, 1997).
  2. W. Xu, X. Liu, and Y. Gong, SIGIR Forum (Toronto, Canada, 2003) p.267.
  3. D. D. Lee and H. S. Seung, Nature 401, 788 (1999).
  4. S. Wild, J. Curry, and A. Dougherty, Pattern Recognition 37, 2217 (2004).
  5. I. S. Dhillon and D. S. Modha, Machine Learning 42, 143 (2001).
  6. D. Guillamet and J. Vitria, in Proceedings of 11th International Conference on Image Analysis and Processing 256 (2001).
  7. R. Zhao and W. I. Grosky, Pattern Recognition 35, 593 (2002).
  8. S.-O. Shim and T.-S. Choi, in Proceedings of IEEE International Conference on Image Processing 493 (2003).

Machine Learning

I. S. Dhillon and D. S. Modha, Machine Learning 42, 143 (2001).

Nature

D. D. Lee and H. S. Seung, Nature 401, 788 (1999).

Pattern Recognition

S. Wild, J. Curry, and A. Dougherty, Pattern Recognition 37, 2217 (2004).

R. Zhao and W. I. Grosky, Pattern Recognition 35, 593 (2002).

Other

S.-O. Shim and T.-S. Choi, in Proceedings of IEEE International Conference on Image Processing 493 (2003).

D. Guillamet and J. Vitria, in Proceedings of 11th International Conference on Image Analysis and Processing 256 (2001).

Z. Pecenovic, Image Retrieval Using Latent Semantic Indexing M. Sc. Thesis (Swiss Federal Institute of Technology, Lausanne, 1997).

W. Xu, X. Liu, and Y. Gong, SIGIR Forum (Toronto, Canada, 2003) p.267.

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