Sparse Approximation of Overdetermined Systems for Image Retrieval Application

M, Srinivas and R, Ramu Naidu (2015) Sparse Approximation of Overdetermined Systems for Image Retrieval Application. In: Mathematical Analysis and its Applications. Springer Proceedings in Mathematics & Statistics, 143 . Springer India, pp. 219-227. ISBN 978-81-322-2484-6

Full text not available from this repository. (Request a copy)


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.

[error in script]
IITH Creators:
IITH CreatorsORCiD
Item Type: Book Section
Uncontrolled Keywords: Overdetermined Systems, K-SVD, Image retrieval, LASSO, Underdetermined System,
Subjects: Computer science > Big Data Analytics
?? sub3.8 ??
Divisions: Department of Computer Science & Engineering
Department of Mathematics
Depositing User: Team Library
Date Deposited: 03 Sep 2015 09:44
Last Modified: 03 Sep 2015 09:44
Publisher URL:
Related URLs:

Actions (login required)

View Item View Item
Statistics for RAIITH ePrint 1907 Statistics for this ePrint Item