Distributed image retrieval with colour and keypoint features

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  • Additional Information
    • Publication Information:
      Taylor & Francis Group, 2019.
    • Publication Date:
      2019
    • Collection:
      LCC:Telecommunication
      LCC:Information technology
    • Abstract:
      Content-based image retrieval poses many problems to computer systems. The content of images has to be described by some feature extraction methods. As image databases are often very large, they are sometimes to complex to be processed by traditional computing methods. We have to use big data solutions to fast retrieve images. The paper presents a system for retrieving images in relational databases in a distributed environment. Content of the query image and images in the database is compared using global colour information and local image keypoints. Image keypoint descriptors are indexed by fuzzy sets directly in a relational database by our algorithm. The process is distributed to several machines thanks to the Apache Hadoop software framework with HDFS.
    • File Description:
      electronic resource
    • ISSN:
      2475-1839
      2475-1847
      24751839
    • Relation:
      https://doaj.org/toc/2475-1839; https://doaj.org/toc/2475-1847
    • Accession Number:
      10.1080/24751839.2019.1620023
    • Rights:
      Journal Licence: CC BY-NC
    • Accession Number:
      edsdoj.fde43d1dd364e0b96b9521635db3c69
  • Citations
    • ABNT:
      MICHAŁ ŁA̧GIEWKA; MARCIN KORYTKOWSKI; RAFAL SCHERER. Distributed image retrieval with colour and keypoint features. Journal of Information and Telecommunication, [s. l.], n. 0, p. 1, 2019. Disponível em: . Acesso em: 16 set. 2019.
    • AMA:
      Michał Ła̧giewka, Marcin Korytkowski, Rafal Scherer. Distributed image retrieval with colour and keypoint features. Journal of Information and Telecommunication. 2019;(0):1. doi:10.1080/24751839.2019.1620023.
    • APA:
      Michał Ła̧giewka, Marcin Korytkowski, & Rafal Scherer. (2019). Distributed image retrieval with colour and keypoint features. Journal of Information and Telecommunication, (0), 1. https://doi.org/10.1080/24751839.2019.1620023
    • Chicago/Turabian: Author-Date:
      Michał Ła̧giewka, Marcin Korytkowski, and Rafal Scherer. 2019. “Distributed Image Retrieval with Colour and Keypoint Features.” Journal of Information and Telecommunication, no. 0: 1. doi:10.1080/24751839.2019.1620023.
    • Harvard:
      Michał Ła̧giewka, Marcin Korytkowski and Rafal Scherer (2019) ‘Distributed image retrieval with colour and keypoint features’, Journal of Information and Telecommunication, (0), p. 1. doi: 10.1080/24751839.2019.1620023.
    • Harvard: Australian:
      Michał Ła̧giewka, Marcin Korytkowski & Rafal Scherer 2019, ‘Distributed image retrieval with colour and keypoint features’, Journal of Information and Telecommunication, no. 0, p. 1, viewed 16 September 2019, .
    • MLA:
      Michał Ła̧giewka, et al. “Distributed Image Retrieval with Colour and Keypoint Features.” Journal of Information and Telecommunication, no. 0, 2019, p. 1. EBSCOhost, doi:10.1080/24751839.2019.1620023.
    • Chicago/Turabian: Humanities:
      Michał Ła̧giewka, Marcin Korytkowski, and Rafal Scherer. “Distributed Image Retrieval with Colour and Keypoint Features.” Journal of Information and Telecommunication, no. 0 (2019): 1. doi:10.1080/24751839.2019.1620023.
    • Vancouver/ICMJE:
      Michał Ła̧giewka, Marcin Korytkowski, Rafal Scherer. Distributed image retrieval with colour and keypoint features. Journal of Information and Telecommunication [Internet]. 2019 [cited 2019 Sep 16];(0):1. Available from: http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsdoj&AN=edsdoj.fde43d1dd364e0b96b9521635db3c69&custid=s8280428