A linear-algebraic technique with an application in semantic image retrieval

J.S. Hare, P.H. Lewis, P.G.B. Enser, C.J. Sandom

Research output: Chapter in Book/Conference proceeding with ISSN or ISBNConference contribution with ISSN or ISBN

Abstract

This paper presents a novel technique for learning the underlying structure that links visual observations with semantics. The technique, inspired by a text-retrieval technique known as cross-language latent semantic indexing uses linear algebra to learn the semantic structure linking image features and keywords from a training set of annotated images. This structure can then be applied to unannotated images, thus providing the ability to search the unannotated images based on keyword. This factorisation approach is shown to perform well, even when using only simple global image features.
Original languageEnglish
Title of host publicationImage and video retrieval: 5th international conference, CIVR 2006
EditorsH. Sundaram, M. Naphade, J.R. Smith, R. Yong
Place of PublicationBerlin, Germany
PublisherSpringer
Pages31-40
Number of pages10
Volume4071/2006
ISBN (Print)9783540360186
DOIs
Publication statusPublished - 2006
EventImage and video retrieval: 5th international conference, CIVR 2006 - Tempe, AZ, USA, July 13-15, 2006
Duration: 1 Jan 2006 → …

Publication series

NameLecture notes in computer science

Conference

ConferenceImage and video retrieval: 5th international conference, CIVR 2006
Period1/01/06 → …

Keywords

  • Visual image retrieval
  • semantics

Fingerprint Dive into the research topics of 'A linear-algebraic technique with an application in semantic image retrieval'. Together they form a unique fingerprint.

  • Cite this

    Hare, J. S., Lewis, P. H., Enser, P. G. B., & Sandom, C. J. (2006). A linear-algebraic technique with an application in semantic image retrieval. In H. Sundaram, M. Naphade, J. R. Smith, & R. Yong (Eds.), Image and video retrieval: 5th international conference, CIVR 2006 (Vol. 4071/2006, pp. 31-40). (Lecture notes in computer science). Springer. https://doi.org/10.1007/11788034_4