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Image database systems must effectively and efficiently handle and retrieve images from a large collection of images. A serious problem faced by these systems is the requirement to deal with the nonstationary database. In an image database system, image features are typically organized into an indexing structure, and updating the indexing structure involves many computations. In this paper, this difficult problem is converted into a constrained optimization problem, and the iteration-free clustering (IFC) algorithm based on the Lagrangian function, is presented for adapting the existing indexing structure for a nonstationary database. Experimental results concerning recall and precision indicate that the proposed method provides a binary tree that is almost optimal. Simulation results further demonstrate that the proposed algorithm can maintain 94% precision in seven-dimensional feature space, even when the number of new-coming images is one-half the number of images in the original database. Finally, our IFC algorithm outperforms other methods usually applied to image databases.