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Organizing image databases as visual-content search trees
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DOI：10.1016/S0262-8856(98)00142-5.png)
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An unsupervised algorithm for arranging an image database as a visual-content binary search tree is described. Tree nodes are associated with image subsets, maintaining the property that the similarity among the images associated with the children of a node is higher than the similarity among the images associated with the parent node. Visual-content search trees can be used to automate image retrieval, and help a human to interactively search for images. Experiments with datasets of hundreds and thousands of images show that shallow trees produce clustering into 'meaningful' classes. (C) 1999 Elsevier Science B.V. All rights reserved.
Keyword:
image indexing
video indexing
visual search
visual clustering
digital libraries
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4.2
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