IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v12y2020i11p183-d435816.html
   My bibliography  Save this article

A Knowledge-Driven Multimedia Retrieval System Based on Semantics and Deep Features

Author

Listed:
  • Antonio Maria Rinaldi

    (Department of Electrical Engineering and Information Technologies, University of Naples Federico II, Via Claudio, 21, 80125 Napoli, Italy
    These authors contributed equally to this work.)

  • Cristiano Russo

    (Department of Electrical Engineering and Information Technologies, University of Naples Federico II, Via Claudio, 21, 80125 Napoli, Italy
    These authors contributed equally to this work.)

  • Cristian Tommasino

    (Department of Electrical Engineering and Information Technologies, University of Naples Federico II, Via Claudio, 21, 80125 Napoli, Italy
    These authors contributed equally to this work.)

Abstract

In recent years the information user needs have been changed due to the heterogeneity of web contents which increasingly involve in multimedia contents. Although modern search engines provide visual queries, it is not easy to find systems that allow searching from a particular domain of interest and that perform such search by combining text and visual queries. Different approaches have been proposed during years and in the semantic research field many authors proposed techniques based on ontologies. On the other hand, in the context of image retrieval systems techniques based on deep learning have obtained excellent results. In this paper we presented novel approaches for image semantic retrieval and a possible combination for multimedia document analysis. Several results have been presented to show the performance of our approach compared with literature baselines.

Suggested Citation

  • Antonio Maria Rinaldi & Cristiano Russo & Cristian Tommasino, 2020. "A Knowledge-Driven Multimedia Retrieval System Based on Semantics and Deep Features," Future Internet, MDPI, vol. 12(11), pages 1-20, October.
    • Handle: RePEc:gam:jftint:v:12:y:2020:i:11:p:183-:d:435816
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/12/11/183/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1999-5903/12/11/183/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Stephen P. Harter, 1992. "Psychological relevance and information science," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 43(9), pages 602-615, October.
    2. Carol L. Barry, 1998. "Document representations and clues to document relevance," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 49(14), pages 1293-1303.
    3. Tefko Saracevic, 1975. "RELEVANCE: A review of and a framework for the thinking on the notion in information science," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 26(6), pages 321-343, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Filipe Portela, 2021. "Data Science and Knowledge Discovery," Future Internet, MDPI, vol. 13(7), pages 1-4, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jingfei Li & Peng Zhang & Dawei Song & Yue Wu, 2017. "Understanding an enriched multidimensional user relevance model by analyzing query logs," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(12), pages 2743-2754, December.
    2. Xiaoli Huang & Dagobert Soergel, 2013. "Relevance: An improved framework for explicating the notion," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(1), pages 18-35, January.
    3. Howard D. White, 2010. "Some new tests of relevance theory in information science," Scientometrics, Springer;Akadémiai Kiadó, vol. 83(3), pages 653-667, June.
    4. Nathalie Demoulin & Kristof Coussement, 2018. "Acceptance of text-mining systems: The signaling role of information quality," Post-Print hal-02111772, HAL.
    5. Wayne de Fremery & Michael K. Buckland, 2022. "Context, relevance, and labor," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(9), pages 1268-1278, September.
    6. Ian Ruthven, 2021. "Resonance and the experience of relevance," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(5), pages 554-569, May.
    7. Anton van der Vegt & Guido Zuccon & Bevan Koopman, 2021. "Do better search engines really equate to better clinical decisions? If not, why not?," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(2), pages 141-155, February.
    8. Malcolm Dow & Peter Willett & Roderick McDonald & Belver Griffith & Michael Greenacre & Peter Bryant & Daniel Wartenberg & Ove Frank, 1987. "Book reviews," Journal of Classification, Springer;The Classification Society, vol. 4(2), pages 245-278, September.
    9. Jensen, Scott & Liu, Xiaozhong & Yu, Yingying & Milojevic, Staša, 2016. "Generation of topic evolution trees from heterogeneous bibliographic networks," Journal of Informetrics, Elsevier, vol. 10(2), pages 606-621.
    10. Dietmar Wolfram, 2015. "The symbiotic relationship between information retrieval and informetrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(3), pages 2201-2214, March.
    11. Min Sook Park & JungHo Park & Hyejin Kim & Jin Hui Lee & Hyejin Park, 2023. "Measuring the impacts of quantity and trustworthiness of information on COVID‐19 vaccination intent," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(7), pages 846-865, July.
    12. Anton Oleinik, 2022. "Relevance in Web search: between content, authority and popularity," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(1), pages 173-194, February.
    13. Zhang, Lei & Kopak, Rick & Freund, Luanne & Rasmussen, Edie, 2011. "Making functional units functional: The role of rhetorical structure in use of scholarly journal articles," International Journal of Information Management, Elsevier, vol. 31(1), pages 21-29.
    14. Aurora González-Teruel & Gregorio González-Alcaide & Maite Barrios & María-Francisca Abad-García, 2015. "Mapping recent information behavior research: an analysis of co-authorship and co-citation networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(2), pages 687-705, May.
    15. Olof Sundin & Dirk Lewandowski & Jutta Haider, 2022. "Whose relevance? Web search engines as multisided relevance machines," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(5), pages 637-642, May.
    16. Gineke Wiggers & Suzan Verberne & Wouter van Loon & Gerrit‐Jan Zwenne, 2023. "Bibliometric‐enhanced legal information retrieval: Combining usage and citations as flavors of impact relevance," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(8), pages 1010-1025, August.
    17. Marcia J. Bates, 2011. "Birger Hjørland's Manichean misconstruction of Marcia Bates' work," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(10), pages 2038-2044, October.
    18. P. K. Paul & A. Bhuimali & R. Rajesh & K. L. Dangwal & P. Das & J. Ganguly, 2016. "Green Computing for Eco Enriched Information Services and Systems: Environmental & Bio Informatics Perspective," Journal of Biotechnology Research, Academic Research Publishing Group, vol. 2(6), pages 44-48, 06-2016.
    19. Adan Ortiz-Cordova & Bernard J. Jansen, 2012. "Classifying web search queries to identify high revenue generating customers," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(7), pages 1426-1441, July.
    20. Pin Li & Guoli Yang & Chuanqi Wang, 2019. "Visual topical analysis of library and information science," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(3), pages 1753-1791, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:12:y:2020:i:11:p:183-:d:435816. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.