Abstract
We address the problem of K-Nearest Neighbors (KNN) search in large image databases. Our approach is to cluster the database of n points (i.e. images) using a self-organizing map algorithm. Then, we map each cluster into a point in one-dimensional distance space. From these mapped points, we construct a simple, compact and yet fast index structure, called array-index. Unlike most indexes of KNN algorithms that require storage space exponential in dimensions, the array-index requires a storage space that is linear in the number of generated clusters. Due to the simplicity and compactness of the array-index,the experiments show that our method outperforms other well know methods.
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