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Testing dimension reduction methods for image retrieval

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EN
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In this paper, we compare performance of several dimension reduction techniques, namely LSI, NMF, SDD and FastMap. The qualitative comparison is based on rank lists and evaluated on a collection of faces from the Olivetti Research Lab. We compare the quality of these methods from several standpoints: the visual impact, quality of generated "eigenfaces", size of reduced matrices and retrieval performance.
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autor
  • Department of Computer Science, FEECS, VŠB - Technical University of Ostrava, 17. listopadu 15, 708 33 Ostrava-Poruba, Czech Republic, pavel.moravec@vsb.cz
Bibliografia
  • [1] Face recognition homepage. May, 2006, http://www.face-rec.org.
  • [2] R. Baeza-Yates and B. Ribeiro-Neto. Modern Information Retrieval. Addison Wesley, New York, 1999.
  • [3] M.W. Berry, S.T. Dumais, and T.A. Letsche. Computational Methods for Intelligent Information Access. In Proceedings of the 1995 ACM/IEEE Supercomputing Conference, San Diego, California, USA, 1995.
  • [4] E. Chavez and G. Navarro. A probabilistic spell for the curse of dimensionality. In Proc. 3rd Workshop on Algorithm Engineering and Experiments (ALENEX’0l), LNCS 2153. Springer-Verlag, 2001.
  • [5] C. Faloutsos and K. Lin. FastMap: A Fast Algorithm for Indexing, Data-Mining and Visualization of Traditional and Multimedia Datasets. ACM SIGMOD Record, 24(2):163-174, 1995.
  • [6] Abby A. Goodrum. Image Information Retrieval: An Overview of Current Research. Informing Science, 3(2):63-67, 2000.
  • [7] Gisli R. Hjaltason and Hanan Samet. Properties of embedding methods for similarity searching in metric spaces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(5):530-549, 2003.
  • [8] Tamara G. Kolda and Dianne P. O'Leary. Computation and uses of the semidiscrete matrix decomposition. In ACM Transactions on Information Processing, 2000.
  • [9] P. Praks, L. Machala, and V. Snasel. On SVD-free Latent Semantic Indexing for Iris Recognition of Large Databases. In Multimedia Data mining and Knowledge Discovery; V. A. Petrushin and L. Khan (Eds.), Chapter 26. Springer Verlag.
  • [10] G. Salton and G. McGill. Introduction to Modern Information Retrieval. McGraw-Hill, New York, USA, 1983.
  • [11] F. Shahnaz, M. Berry, P. Pauca, and R. Plemmons. Document clustering using nonnegative matrix factorization. Journal on Information Processing and Management, 42:373-386, 2006.
  • [12] W. Skarbek, K.Kucharski, and M. Bober. Cascade of Dual LDA Operators for Face Recognition. In Geometric Properties for Incomplete Data, pages 199-219, Springer, 2006.
  • [13] M. W. Spratling. Learning Image Components for Object Recognition. Journal of Machine Learning Research, 7:793-815, 2006.
  • [14] M. Turk and A. Pentland. Eigenfaces for recognition. Journal of Cognitive Neuro-science, 3(l):71-86, 1991.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BAT5-0017-0079
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