Application of l1 norm minimization technique to image retrieval
Inhaltsverzeichnis
Referenz
Sastry, Saurabah Jain and Ashish Mishra: Application of l1 norm minimization technique to image retrieval. WASET, Vol.56, No.145, pp.801-804, 2009
DOI
Abstract
Image retrieval is a topic where scientific interest is currently high. The important steps associated with image retrieval system are the extraction of discriminative features and a feasible similarity metric for retrieving the database images that are similar in content with the search image. Gabor filtering is a widely adopted technique for feature extraction from the texture images. The recently proposed sparsity promoting l1-norm minimization technique finds the sparsest solution of an under-determined system of linear equations. In the present paper, the l1-norm minimization technique as a similarity metric is used in image retrieval. It is demonstrated through simulation results that the l1-norm minimization technique provides a promising alternative to existing similarity metrics. In particular, the cases where the l1-norm minimization technique works better than the Euclidean distance metric are singled out.
Extended Abstract
Bibtex
@article{
author = {Sastry, Saurabah Jain and Ashish Mishra},
title = {Application of l1 norm minimization technique to image retrieval},
journal = {WASET, World Academy of Science, Engineering and Technology International Journal of Computer, Electrical, Automation, Control and Information Engineering},
volume = {56},
number = {145},
pages = {801-804},
year = {2009},
doi={},
url={http://waset.org/publications/13329/application-of-l1-norm-minimization-technique-to-image-retrieval http://de.evo-art.org/index.php?title=Application_of_l1_norm_minimization_technique_to_image_retrieval},
}
Used References
[1] P. Brodatz, Textures: A photographic album for artists and designers, Dover Publication, New York, 1996.
[2] A. M. Bruckstein, D. L. Donoho and M. Elad, From sparse solutions of systems of equations to sparse modeling of signals and images, SIAM Review, vol. 51, No. 1, pp: 34-81, 2009.
[3] E. Candes and J. Romberg, l1 magic: Recovery of sparse signals via convex programming, http://www.acm.caltech.edu/l1magic/, 2005.
[4] E. Candes and J. Romberg and T. Tao, Stable signal recovery from incomplete and inaccurate measurements, Comm. Pure and Applied Maths, 59, 1207–1223, 2006.
[5] S. Chen and D. Donoho and M. Saunders, Atomic decomposition by basis pursuit, SIAM Review, 43(1), 129–159, 2001.
[6] D. Donoho, For most large underdetermined systems of linear equations the minimal l1-norm near solution approximates the sparsest solution, Comm. Pure and Applied Maths, 59(10), 907–34, 2006.
[7] B. S. Manjunath and Y. S. Ma, Texture features for browsing and retrieval of image data, IEEE T. on Pattern Analysis and Machine Intelligence, 18(8):837–842, 1996.
[8] C. S. Sastry and M. Ravindranath and A. K. Pujari B. L. Deekshatulu, A modified Gabor method for content based image retrieval, Pattern Recognition Letters, 28, 293–300, 2007.
Links
Full Text
http://waset.org/publications/13329/application-of-l1-norm-minimization-technique-to-image-retrieval
