Efficient retrieval for browsing large image databases

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Abstract
The management of large image databases poses several interesting and challenging problems. These problems range from ingesting the data and extracting meta-data to the efficient storage and retrieval of the data. Of particular interest are the retrieval methods and user interactions with an image database during browsing. In image databases, the response to a given query is not an exact well-defined set, rather, the user poses a query and expects a set of responses that should contain many possible candidates from which the user chooses the answer set. We first present the browsing model in Alexandria, a digital library for maps and satellite images. Designed for content-based retrieval, the relevant information in an image is encoded in the form of a multi-% dimensional feature vector. Various techniques have been previously proposed for the efficient retrieval of such vectors by reducing the dimensionality of such vectors. We show that for even moderately large databases (in fact, only 1856 texture images), these approaches do not scale well for exact retrieval. However, as a browsing tool, these dimensionality reduction techniques hold much promise.

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