Abstract
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.
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Notes
- 1.
Viewing the problem of image retrieval as an extension of classification, in this paper, we use the words “classification” and “retrieval” synonymous. This is of course a slight abuse of convention.
- 2.
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Acknowledgments
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.
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Srinivas, M., Naidu, R.R. (2015). Sparse Approximation of Overdetermined Systems for Image Retrieval Application. In: Agrawal, P., Mohapatra, R., Singh, U., Srivastava, H. (eds) Mathematical Analysis and its Applications. Springer Proceedings in Mathematics & Statistics, vol 143. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2485-3_17
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DOI: https://doi.org/10.1007/978-81-322-2485-3_17
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