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In allusion to similarity calculation difficulty caused by high maintenance of image data, this paper introduces sparse principal component algorithm to figure out embedded subspace after dimensionality reduction of image visual words on the basis of traditional spectral hashing image index method so that image high-dimension index results can be explained overall. This method is called sparse spectral...
Existing linear projection based hashing methods have witnessed many progresses in finding the approximate nearest neighbor(s) of a given query. They perform well when using a short code. But their code length depends on the original data dimension, thus their performance can not be further improved with higher number of bits for low dimensional data. In addition, in the case of high dimensional data,...
In this paper, we propose several improvements to the 3D-tarp coder for the lossy compression of hyperspectral imagery. Specific ameliorations include use of principal component analysis instead of a wavelet transform for spectral decorrelation, use of the quincunx wavelet transform instead of the traditional dyadic decomposition in the spatial direction, and spectral partitioning with skipping of...
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