BoostMap: A Method for Efficient Approximate Similarity Rankings

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BoostMap: A Method for Efficient Approximate Similarity Rankings (2003)

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by V. Athitsos , Jonathan Alon , Stan Sclaroff , George Kollios

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112 - 13 self

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@MISC{Athitsos03boostmap:a,
    author = {V. Athitsos and Jonathan Alon and Stan Sclaroff and George Kollios},
    title = {BoostMap: A Method for Efficient Approximate Similarity Rankings},
    year = {2003}
}

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Abstract

This paper introduces BoostMap, a method that can significantly reduce retrieval time in image and video database systems that employ computationally expensive distance measures, metric or non-metric. Database and query objects are embedded into a Euclidean space, in which similarities can be rapidly measured using a weighted Manhattan distance. Embedding construction is formulated as a machine learning task, where AdaBoost is used to combine many simple, 1D embeddings into a multidimensional embedding that preserves a significant amount of the proximity structure in the original space. Performance is evaluated in a hand pose estimation system, and a dynamic gesture recognition system, where the proposed method is used to retrieve approximate nearest neighbors under expensive image and video similarity measures. In both systems, BoostMap significantly increases efficiency, with minimal losses in accuracy. Moreover, the experiments indicate that BoostMap compares favorably with existing embedding methods that have been employed in computer vision and database applications, i.e., FastMap and Bourgain embeddings.

Keyphrases

efficient approximate similarity ranking dynamic gesture recognition system original space expensive distance measure proximity structure query object database application video similarity measure multidimensional embedding video database system many simple hand pose estimation system computer vision expensive image significant amount retrieval time minimal loss weighted manhattan distance bourgain embeddings euclidean space

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