Multi-step processing of spatial joins
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Similarity-based queries for time series data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Distance-based indexing for high-dimensional metric spaces
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Multidimensional access methods
ACM Computing Surveys (CSUR)
Classification with Nonmetric Distances: Image Retrieval and Class Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
Data Structures for Range Searching
ACM Computing Surveys (CSUR)
The K-D-B-tree: a search structure for large multidimensional dynamic indexes
SIGMOD '81 Proceedings of the 1981 ACM SIGMOD international conference on Management of data
Searching in metric spaces with user-defined and approximate distances
ACM Transactions on Database Systems (TODS)
A Multistep Approach for Shape Similarity Search in Image Databases
IEEE Transactions on Knowledge and Data Engineering
Efficient Color Histogram Indexing for Quadratic Form Distance Functions
IEEE Transactions on Pattern Analysis and Machine Intelligence
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Fast Nearest Neighbor Search in Medical Image Databases
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
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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.