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We
describe a novel system that strives to achieve advanced content-based
image retrieval using seamless combination of two complementary
approaches: on one hand, we propose a new color clustering method
to better capture color properties of the original images; on
the other hand, expecting that image regions acquired from the
original images inevitably contain many errors, we make use of
the available erroneous, ill-segmented image regions to accomplish
the object region-based image retrieval. We also propose an effective
image indexing scheme to facilitate fast and efficient image matching
and retrieval. The carefully designed experimental evaluation
shows that our proposed image retrieval system surpasses other
methods under the comparison in terms of not only quantitative
measures, but also image retrieval capabilities.
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