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Abstract:When very large image families are involved in query processes, methods of content-based image retrieval must be optimized with a goal function determining a computing complexity. A clustering method which at the image retrieval stage ensure minimal number of comparisons of a query image and images from image database is proposed. Clustering can be fulfilled in feature or signal space. Pointwise set maps are used as the tools to find required partitions.Key words: image retrieval; clustering; number of matches; pointwise set map. INTRODUCTIONSoftware and hardware development of image acquisition, storage, proc essing, recognition, display and communication produces a background to make image databases affordable and what is more prevalent conformably to a number of applications. Highest information capacity of imagery data emphasizes barest necessity of advanced research as well. However the effecttiveness of the content-based image retrieval (CBIR) depends on chosen feature space, as evaluation of image similarities in database and a query image is computationally expensive. Moreover, when very large image collections are in use, even though reduced dimensions of high-level features do not ensure efficient query processing. One of widespread approaches to minimize time outlay consists inapplicationofvarioustechniquesof preliminary processing of image database including clustering methods 1,2,3,4,5,6 and so mediated or direct matching is one of the central problems in CBIR. Such 946 K. Wojciechowski et al. (eds.), Computer Vision and Graphics,[946][947][948][949][950][951][952]

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Image processing for the efficient retrieval should perform the ability of data granulation and interpretation. In this paper the properties of metric on nested partitions which allow to analyze objects represented at different levels of granularity and abstraction is considered. It also ensures a retrieval of image parts corresponding to the searched objects i.e. provides a search criterion for background independent objects.

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