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    On the Use of Statistical Semantics for Metadata-Based Social Image Retrieval


    Rekabsaz, Navid, Bierig, Ralf, Ionescu, Bogdan, Hanbury, Allan and Lupu, Mihai (2015) On the Use of Statistical Semantics for Metadata-Based Social Image Retrieval. 2015 13th International Workshop on Content-Based Multimedia Indexing (CBMI). pp. 1-4. ISSN 1949-3991

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    Abstract

    We revisit text-based image retrieval for social media, exploring the opportunities offered by statistical semantics. We assess the performance and limitation of several complementary corpus-based semantic text similarity methods in combination with word representations. We compare results with state-of-the-art text search engines. Our deep learning-based semantic retrieval methods show a statistically significant improvement in comparison to a best practice Solr search engine, at the expense of a significant increase in processing time. We provide a solution for reducing the semantic processing time up to 48% compared to the standard approach, while achieving the same performance.
    Item Type: Article
    Keywords: Semantics; Image retrieval; Indexing; Context; Encyclopedias; Correlation;
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 15205
    Identification Number: 10.1109/CBMI.2015.7153634
    Depositing User: Ralf Bierig
    Date Deposited: 10 Jan 2022 13:26
    Journal or Publication Title: 2015 13th International Workshop on Content-Based Multimedia Indexing (CBMI)
    Publisher: IEEE
    Refereed: Yes
    Related URLs:
    URI: https://mural.maynoothuniversity.ie/id/eprint/15205
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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