2D-LPI: Two-Dimensional Locality Preserving Indexing

  • S. Manjunath
  • D. S. Guru
  • M. G. Suraj
  • R. Dinesh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5909)

Abstract

In this paper, we present a new model called two-dimensional locality preserving indexing (2D-LPI) for image recognition. The proposed model gives a new dimension to the conventional locality preserving indexing (LPI). Unlike the conventional method the proposed method can be applied directly on images in 2D plane. In order to corroborate the efficacy of the proposed method extensive experimentation has been carried out on various domains such as video summarization, face recognition and fingerspelling recognition. In video summarization we comapre the proposed method only with 2D-LPP which was recently used for video summarization. In face recognition and fingerspelling recognition we compare the proposed method with the conventional LPI and also with the existing two-dimensional subspace methods viz., 2D-PCA, 2D-FLD and 2D-LPP.

Keywords

Face Recognition Latent Semantic Analysis Latent Semantic Indexing Video Summarization Locality Preserve Projection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by latent semantic analysis. Journal of the American Society of Information Science 41, 391–407 (1990)CrossRefGoogle Scholar
  2. 2.
    Kurimo, M.: Indexing audio documents by using latent semantic analysis and som. In: Kohonen maps, pp. 363–374. Elsevier, Amsterdam (1999)CrossRefGoogle Scholar
  3. 3.
    Zhao, R., Grosky, W.I.: From features to semantics: Some preliminary results. In: Proceedings of International Conference on Multimedia and Expo (2000)Google Scholar
  4. 4.
    Souvannavong, F., Merialdo, B., Huet, B.: Video content modeling with latent semantic analysis. In: Proceedings of Third International Workshop on Content-based Multimedia Indexing (2003)Google Scholar
  5. 5.
    He, X., Cai, D., Liu, H., Ma, W.Y.: Locality preserving indexing for document representation. In: Proceedings of International Conference on Research and Development in Information Retrieval, pp. 96–103 (2004)Google Scholar
  6. 6.
    He, X., Niyogi, P.: Locality preserving projections. In: Advances in Neural Information Processing Systems (2003)Google Scholar
  7. 7.
    Cai, D., He, X., Han, J.: Document clustering using locality preserving indexing. IEEE Transactions on Knowledge and Data Engineering 17, 1624–1637 (2005)CrossRefGoogle Scholar
  8. 8.
    Yang, J., Zhang, D., Frangi, A.F., yu Yang, J.: Two-dimensional pca a new approach to appearance-based face representation and recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(1), 131–137 (2004)CrossRefGoogle Scholar
  9. 9.
    Li, M., Yuan, B.: 2d-lda a statistical linear discriminant analysis for image matrix. Pattern Recognition 26(5), 527–532 (2005)CrossRefGoogle Scholar
  10. 10.
    Chen, S., Zhao, H., Kong, M., Luo, B.: 2d-lpp a two dimensional extension of locality preserving projections. Neurocomputing 70, 912–921 (2007)CrossRefGoogle Scholar
  11. 11.
    Xu, L.Q., Luo, B.: Appearance-based video clustering in 2d locality preserving projection subspace. In: Proceedings of ACM International Conference on Image and Vide Retrieval (2007)Google Scholar
  12. 12.
    Guru, D.S., Suraj, M.G.: Fusion of pca and fld at feature extraction level for finger spelling recognition. In: Proceedings of the Third Indian International Conference on Atrificial Intelligence (IICAI), pp. 2113–2123 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • S. Manjunath
    • 1
  • D. S. Guru
    • 1
  • M. G. Suraj
    • 1
  • R. Dinesh
    • 2
  1. 1.Department of Studies in Computer ScienceUniversity of MysoreMysoreIndia
  2. 2.Honeywell Technology SolutionsBengaluruIndia

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