Exploiting sparse representations in very high-dimensional feature spaces obtained from patch-based processing |
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Authors: | J E Hunter M Tugcu X Wang C Costello D M Wilkes |
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Affiliation: | (1) Language and Media Processing (LAMP), University of Maryland Institute for Advanced Computer Studies (UMIACS), College Park, MD 20742, USA |
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Abstract: | Use of high-dimensional feature spaces in a system has standard problems that must be addressed such as the high calculation
costs, storage demands, and training requirements. To partially circumvent this problem, we propose the conjunction of the
very high-dimensional feature space and image patches. This union allows for the image patches to be efficiently represented
as sparse vectors while taking advantage of the high-dimensional properties. The key to making the system perform efficiently
is the use of a sparse histogram representation for the color space which makes the calculations largely independent of the
feature space dimension. The system can operate under multiple L
p
norms or mixed metrics which allows for optimized metrics for the feature vector. An optimal tree structure is also introduced
for the approximate nearest neighbor tree to aid in patch classification. It is shown that the system can be applied to various
applications and used effectively. |
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Keywords: | |
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