Application of l1-Norm Minimization Technique to Image Retrieval
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32845
Application of l1-Norm Minimization Technique to Image Retrieval

Authors: C. S. Sastry, Saurabh Jain, Ashish Mishra


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.

Keywords: l1-norm minimization, content based retrieval, modified Gabor function.

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3394


[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,, 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.