21 September 1998 Parallel methods for similar image compression and classification with common models
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Abstract
This paper addresses efficient parallel compression and classification for sets of similar images that are normally generated from satellite imagery, medical imaging (CT and MR scans) or aerial surveillance. From our experiments it was observed that image similarities for each class of images can be more efficiently expressed in the domain of image compressing transforms. In particular, the paper shows that only one predictive compressing model can be constructed for the entire class of similar images of the same nature, and then used for nearly optimal compression of any image of the class. The extraction of the optimal class-compressing model still remains a computationally intensive process, which can be considerably improved on parallel computers. The paper demonstrates how a similar database compressing model can be extracted in parallel, and how this can be used for parallel similar database compression and classification of new images into appropriate similarity classes. The results of the parallel similar image analysis are demonstrated with MR and CT brain images obtained from the M.D. Anderson Cancer Center.
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Oleg S. Pianykh, John M. Tyler, Raj Sharman, "Parallel methods for similar image compression and classification with common models", Proc. SPIE 3452, Parallel and Distributed Methods for Image Processing II, (21 September 1998); doi: 10.1117/12.323464; https://doi.org/10.1117/12.323464
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