Learning Integrated Online Indexing for Image Databases

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Most of the current image retrieval systems use 'one-shot' queries to a database to retrieve similar images. Typically a K-NN (nearest neighbor) kind of algorithms is used where weights measuring feature importance along input dimensions remain fixed (or manually tweaked by the user) in the computation of a given similarity metric. However, the similarity does not vary with equal strength or in the same proportion in all directions in the feature space emanating from the query image. The manual adjustment of these weights is time consuming and exhausting. Moreover, it requires a very sophisticated user. In this paper we present a novel method that enables image retrieval procedures to continuously learn feature relevance based on user's feedback, and which is highly adaptive to query locations. Experimental results are presented that provide the objective evaluation of learning behavior of the method for image retrieval.

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