Diverse M-Best Solutions by Dynamic Programming

Many computer vision pipelines involve dynamic programming primitives such as finding a shortest path or the minimum energy solution in a tree-shaped probabilistic graphical model. In such cases, extracting not merely the best, but the set of M-best solutions is useful to generate a rich collection...

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Bibliographic Details
Published inPattern Recognition pp. 255 - 267
Main Authors Haubold, Carsten, Uhlmann, Virginie, Unser, Michael, Hamprecht, Fred A.
Format Book Chapter
LanguageEnglish
Published Cham Springer International Publishing
SeriesLecture Notes in Computer Science
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Summary:Many computer vision pipelines involve dynamic programming primitives such as finding a shortest path or the minimum energy solution in a tree-shaped probabilistic graphical model. In such cases, extracting not merely the best, but the set of M-best solutions is useful to generate a rich collection of candidate proposals that can be used in downstream processing. In this work, we show how M-best solutions of tree-shaped graphical models can be obtained by dynamic programming on a special graph with M layers. The proposed multi-layer concept is optimal for searching M-best solutions, and so flexible that it can also approximate M-best diverse solutions. We illustrate the usefulness with applications to object detection, panorama stitching and centerline extraction.
Bibliography:Electronic supplementary materialThe online version of this chapter (doi:10.1007/978-3-319-66709-6_21) contains supplementary material, which is available to authorized users.
ISBN:3319667084
9783319667089
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-319-66709-6_21