Sparse Approximation of Overdetermined Systems for Image Retrieval Application

  • M. Srinivas
  • R. Ramu Naidu
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 143)


The recent developments in the field of compressed sensing (CS) have been shown to have tremendous potential for applications such as content-based image retrieval. The underdetermined framework present in CS requires some implicit assumptions on the image database or needs the projection (or downsampling) of database members into lower dimensional space. The present work, however, poses the problem of image retrieval in overdetermined setting. The main feature of the proposed method is that it does not require any downsampling operation or implicit assumption on the databases. Our experimental results demonstrate that our method has potential for such applications as content-based image retrieval.


Overdetermined Systems K-SVD Image retrieval LASSO  Underdetermined System 



Authors are thankful to Dr. C. Krishna Mohan, Dr. Phanindra Jampana and Dr. C.S. Sastry for fruitful discussions. Authors would like to thank Dr. T.M. Deserno, Department of Medical Informatics, RWTH Aachen, Germany for making the original IRMA Database available for research purposes.


  1. 1.
    Chen, Y., Sastry, C.S., Patel, V., Philips, J., Chellappa, R.: In-Plane rotation and scale invariant simultaneous dictionary learning and clustering. IEEE Trans. Image Process. 22(6), 2166–2180 (2013)CrossRefMathSciNetGoogle Scholar
  2. 2.
    Patel, V.M., Wu, T., Biswas, S., Philips, P.J., Chellappa, R.: Dictionary-based face recognition under variable lighting and pose. IEEE Trans. Inform. Forensics Security 7(3), 954–965 (2012)Google Scholar
  3. 3.
    Elad, M.: Sparse and Redundant Representations. Springer, New York (2010)Google Scholar
  4. 4.
    Sprechmann, P., Sapiro, G.: Dictionary learning and sparse coding for unsupervised clustering. In Proceedings of ICASSP (2010)Google Scholar
  5. 5.
    Aharon, M., Elad, M., Bruckstein, A.M.: The k-svd: an algorithm for designing of over-complete dictionaries for sparse representation. IEEE Trans. Signal Process. 54(11), 4311–4322 (2006)CrossRefGoogle Scholar
  6. 6.
    K-Delgado, K., Murray, J.F., Rao, B.D., Engan, K., Lee, T.W., Sejnowski, T.J.: Dictionary learning algorithms for sparse representation. Neural Comput. 15, 349–396 (2003)Google Scholar
  7. 7.
    Tibshirani, R.: Regression shrinkage and selection via the LASSO. J. Statis. Soft. 33(1), 1 (2010)Google Scholar
  8. 8.
    Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31(2), 210–227 (2009)Google Scholar
  9. 9.
    Baraniuk, R., Davenport, M., DeVore, R., Wakin, M.: A simple proof of the restricted isometry property for random matrices. Constr. Approximation 28(3), 253–263 (2008)Google Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringIIT HyderabadHyderabadIndia
  2. 2.Department of MathematicsIIT HyderabadHyderabadIndia

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