A multi-step strategy for approximate similarity search in image databases

Many strategies for similarity search in image databases assume a metric and quadratic form-based similarity model where an optimal lower bounding distance function exists for filtering. These strategies are mainly two-step, with the inital "filter" step based on a spatial or metric access method followed by a "refine" step employing expensive computation. Recent research on robust matching methods for computer vision has discovered that similarity models behind human visual judgment are inherently non-metric. When applying such models to similarity search in image databases, one has to address the problem of nonmetric distance functions that might to have an optimal lower bound for filtering. Here, we propose a novel three-step "prune-filter-refine" strategy for approximate similarity search on these models. First, the "prune" step adopts a spatial access method to roughly eliminate improbable matches via an adjustable distance threshold. Second, the "filter" step uses a quasi lower-bounding distance derived from the non-metric distance function of the similarity model. Third, the "refine" stage compares the query with the remaining candidates by a robust matching method for final ranking. Experimental results confirmed that the proposed strategy achieves more filtering than a two-step approach with close to no false drops in the final result.