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Quantum vs classical ranking in segment grouping

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Title of host publicationStructural, Syntactic, and Statistical Pattern Recognition
DatePublished - 1 Jan 2014
Pages203-212
Number of pages10
PublisherSpringer-Verlag
Volume8621 LNCS
Original languageEnglish
ISBN (Print)9783662444146

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8621 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Abstract

In this paper we explore the use of ranking as a mean of guiding unsupervised image segmentation. Starting by the well known Pagerank algorithm we introduce an extension based on quantum walks. Pagerank (rank) can be used to prioritize the merging of segments embedded in uniform regions (parts of the image with roughly similar appearance statistics). Quantum Pagerank, on the other hand, gives high priority to boundary segments. This latter effect is due to the higher order interactions captured by quantum fluctuations. However we found that qrank does not always outperform its classical version. We analyze the Pascal VOC database and give Intersection over Union (IoU) performances.

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© Springer Verlag 2014. This is an author produced version of a paper published in Lecture Notes in Computer Science. Uploaded in accordance with the publisher's self-archiving policy.

    Research areas

  • quantum walks, random walks, ranking, segment grouping

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