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Image similarity using sparse representation and compression distance
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Guha, Tanaya and Ward, Rabab K. (2014) Image similarity using sparse representation and compression distance. IEEE Transactions on Multimedia, 16 (4). pp. 980-987. doi:10.1109/TMM.2014.2306175 ISSN 1520-9210.
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WRAP-image-similarity-sparse-representation-compression-distance-Guha-2018.pdf - Accepted Version - Requires a PDF viewer. Download (1349Kb) | Preview |
Official URL: http://dx.doi.org/10.1109/TMM.2014.2306175
Abstract
A new line of research uses compression methods to measure the similarity between signals. Two signals are considered similar if one can be compressed significantly when the information of the other is known. The existing compression-based similarity methods, although successful in the discrete one dimensional domain, do not work well in the context of images. This paper proposes a sparse representation-based approach to encode the information content of an image using information from the other image, and uses the compactness (sparsity) of the representation as a measure of its compressibility (how much can the image be compressed) with respect to the other image. The sparser the representation of an image, the better it can be compressed and the more it is similar to the other image. The efficacy of the proposed measure is demonstrated through the high accuracies achieved in image clustering, retrieval and classification.
| Item Type: | Journal Article | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) | ||||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||||
| Library of Congress Subject Headings (LCSH): | Image compression, Kolmogorov complexity | ||||||||
| Journal or Publication Title: | IEEE Transactions on Multimedia | ||||||||
| Publisher: | IEEE | ||||||||
| ISSN: | 1520-9210 | ||||||||
| Official Date: | June 2014 | ||||||||
| Dates: |
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| Volume: | 16 | ||||||||
| Number: | 4 | ||||||||
| Page Range: | pp. 980-987 | ||||||||
| DOI: | 10.1109/TMM.2014.2306175 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Reuse Statement (publisher, data, author rights): | © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||||
| Date of first compliant deposit: | 20 December 2018 | ||||||||
| Date of first compliant Open Access: | 20 December 2018 |
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