Current feature-based image databases can typically perform efficient and effective searches on scalar feature information. However, many important features, such as graphs, histograms, and probability density functions, have more complex structure. Mechanisms to manipulate complex feature data are not currently well understood and must be further developed. The work we discuss in this paper explores techniques for the exploitation of spectral distribution information in a feature-based image database. A six band image was segmented into regions and spectral information for each region was maintained. A similarity measure for the spectral information is proposed and experiments are conducted to test its effectiveness. The objective of our current work is to determine if these techniques are effective and efficient at managing this type of image feature data.
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