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Content-based image retrieval (CBIR) addresses the problem of finding images relevant to the users' information needs, based principally on low-level visual features for which automatic extraction methods are available. For the development of CBIR applications, an important issue is to have efficient and objective performance assessment methods for different features and techniques. In this paper, we study the efficiency of clustering methods for image indexing with entropy-based measures. Furthermore, the self-organizing map (SOM) as an indexing method is discussed further and an analysis method that takes into account also the spatial configuration of the data on the SOM is presented. The proposed methods enable computationally light measurement of indexing and retrieval performance for individual image features.