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Similarity Search with Earth Mover's Distance at Scale

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Similarity Search with Earth Mover's Distance at Scale by Yu Tang
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This dissertation, "Similarity Search With Earth Mover's Distance at Scale" by Yu, Tang, 唐宇, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Earth Mover's Distance (EMD), as a similarity measure, has received a lot of attention in the fields of multimedia and probabilistic databases, computer vision, image retrieval, machine learning, etc. EMD on multidimensional histograms provides better distinguishability between the objects approximated by the histograms (e.g., images), compared to classic measures like Euclidean distance. Despite its usefulness, EMD has a high computational cost; therefore, a number of effective filtering methods have been proposed, to reduce the pairs of histograms for which the exact EMD has to be computed, during similarity search. Still, EMD calculations in the refinement step remain the bottleneck of the whole similarity search process. In this thesis, we focus on optimizing the refinement phase of EMD-based similarity search by (i) adapting an efficient min-cost flow algorithm (SIA) for the EMD computation, (ii) proposing a dynamic distance bound, which is progressively updated and tightened during the refinement process and can be used to terminate an EMD refinement early, and (iii) proposing a dynamic refinement order for the candidates which, paired with a concurrent EMD refinement strategy, reduces the amount of needless computations. Our proposed techniques are orthogonal to and can be easily integrated with the state-of-the-art filtering techniques, reducing the cost of EMD-based similarity queries by orders of magnitude. DOI: 10.5353/th_b5204926 Subjects: Computer algorithmsInformation retrievalImage processingElectronic information resource searching
Release date NZ
January 26th, 2017
Author
Contributor
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Illustrations
colour illustrations
Country of Publication
United States
Imprint
Open Dissertation Press
Dimensions
216x279x4
ISBN-13
9781361342350
Product ID
26644910

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