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        <title type="main">Image Understanding by Socializing the Semantic Gap</title>
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            <forename>Tiberio</forename>
            <surname>Uricchio</surname>
            <placeName type="affiliation">University of Florence, Italy</placeName>
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        <publisher>Firenze University Press</publisher>
        <pubPlace>Florence</pubPlace>
        <date when="2017">2017</date>
        <idno type="DOI">https://doi.org/10.36253/978-88-6453-577-7</idno>
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          <p>Available for academic research purposes</p>
          <p>Open Access</p>
          <p>Copyright Author(s)</p>
          <licence source="text" target="https://creativecommons.org/licenses/by/4.0/legalcode">
            <p>Content licence CC BY 4.0</p>
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        <title>Premio Tesi di Dottorato</title>
        <idno type="ISSN" subtype="print">2612-8039</idno>
        <idno type="ISSN" subtype="electronic">2612-8020</idno>
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          <date>2017</date>
          <idno type="ISBN" subtype="electronic">978-88-6453-577-7</idno>
          <biblScope unit="page">150 pages</biblScope>
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            <p>This is original content, published in Open Access. It is also available to read for free online at <ref target="https://media.fupress.com/files/pdf/24/3505/3505_14489">https://media.fupress.com/files/pdf/24/3505/3505_14489</ref></p>
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          <date>2017</date>
          <idno type="ISBN" subtype="electronic">978-88-9273-164-6</idno>
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            <p>It is available to read for free online</p>
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          <edition n="3">Print edition</edition>
          <date>2017</date>
          <idno type="ISBN" subtype="print">978-88-6453-576-0</idno>
          <biblScope unit="page">150 pages</biblScope>
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        <tag>peer-reviewed</tag>
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      <abstract xml:lang="en">
        <p>Several technological developments like the Internet, mobile devices and Social Networks have spurred the sharing of images in unprecedented volumes, making tagging and commenting a common habit. Despite the recent progress in image analysis, the problem of Semantic Gap still hinders machines in fully understand the rich semantic of a shared photo. In this book, we tackle this problem by exploiting social network contributions. A comprehensive treatise of three linked problems on image annotation is presented, with a novel experimental protocol used to test eleven state-of-the-art methods. Three novel approaches to annotate, under stand the sentiment and predict the popularity of an image are presented. We conclude with the many challenges and opportunities ahead for the multimedia community.</p>
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      <p>It is available online at https://doi.org/10.36253/978-88-6453-577-7<ref target="https://doi.org/10.36253/978-88-6453-577-7" /></p>
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