Visual analogies: A framework for defining aspect categorization

P. Daphne Tsatsoulis, Bryan A. Plummer, David Forsyth

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Analogies are common simple word problems (calf is to cow as x is to sheep?) and we use them to identify analogies between images. Let I[A, θ] be an image of object A at view θ. We show how to learn to choose an image I such that I[A, φ] is to I[A, θ] as I is to I[B, θ]. We introduce a framework to identify an image of a familiar object at an unfamiliar angle and extend our method to treat unfamiliar objects. By doing so, we identify pairs of objects that are good at finding new views of one another. This yields an operational notion of aspectual equivalence: objects are equivalent if they can predict each other’s appearance well.

Original languageEnglish (US)
Title of host publicationComputer Vision – ECCV 2016 Workshops, Proceedings
EditorsGang Hua, Herve Jegou
PublisherSpringer-Verlag
Pages540-547
Number of pages8
ISBN (Print)9783319494081
DOIs
StatePublished - Jan 1 2016
Event14th European Conference on Computer Vision, ECCV 2016 - Amsterdam, Netherlands
Duration: Oct 11 2016Oct 14 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9915 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other14th European Conference on Computer Vision, ECCV 2016
CountryNetherlands
CityAmsterdam
Period10/11/1610/14/16

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Visual analogies: A framework for defining aspect categorization'. Together they form a unique fingerprint.

  • Cite this

    Tsatsoulis, P. D., Plummer, B. A., & Forsyth, D. (2016). Visual analogies: A framework for defining aspect categorization. In G. Hua, & H. Jegou (Eds.), Computer Vision – ECCV 2016 Workshops, Proceedings (pp. 540-547). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9915 LNCS). Springer-Verlag. https://doi.org/10.1007/978-3-319-49409-8_47