DynDex: a dynamic and non-metric space indexer

To date, almost all research work in the Content-Based Image Retrieval (CBIR) community has used Minkowski-like functions to measure similarity between images. In this paper, we first present a non-metric distance function, dynamic partial function (DPF), which works significantly better than Minkowski-like functions for measuring perceptual similarity; and we explain DPF's link to similarity theories in cognitive science. We then propose DynDex, an indexing method that deals with both the dynamic and non-metric aspects of the distance function. DynDex employs statistical methods including distance-based classification and bagging to enable efficient indexing with DPF. In addition to its efficiency for conducting similarity searches in very high-dimensional spaces, we show that DynDex remains effective when features are weighted dynamically for supporting personalized searches.

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