Optimal factor analysis and applications to content-based image retrieval
Computer Vision and Computer Graphics. Theory and Applications: International …, 2008•Springer
We formulate and develop computational strategies for Optimal Factor Analysis (OFA), a
linear dimension reduction technique designed to learn low-dimensional representations
that optimize discrimination based on the nearest-neighbor classifier. The methods are
applied to content-based image categorization and retrieval using a representation of
images by histograms of their spectral components. Various experiments are carried out and
the results are compared to those that have been previously reported for some other image …
linear dimension reduction technique designed to learn low-dimensional representations
that optimize discrimination based on the nearest-neighbor classifier. The methods are
applied to content-based image categorization and retrieval using a representation of
images by histograms of their spectral components. Various experiments are carried out and
the results are compared to those that have been previously reported for some other image …
Abstract
We formulate and develop computational strategies for Optimal Factor Analysis (OFA), a linear dimension reduction technique designed to learn low-dimensional representations that optimize discrimination based on the nearest-neighbor classifier. The methods are applied to content-based image categorization and retrieval using a representation of images by histograms of their spectral components. Various experiments are carried out and the results are compared to those that have been previously reported for some other image retrieval systems.
Springer
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