1 Ocak 2017 Dergi makalesi Açık Erişim

Kilickaya, Mert; Akkus, Burak Kerim; Cakici, Ruket; Erdem, Aykut; Erdem, Erkut; Ikizler-Cinbis, Nazli

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        "affiliation": "Hacettepe Univ, Dept Comp Engn, Ankara, Turkey", 
        "name": "Kilickaya, Mert"
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        "affiliation": "Middle East Tech Univ, Dept Comp Engn, Ankara, Turkey", 
        "name": "Akkus, Burak Kerim"
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      {
        "affiliation": "Middle East Tech Univ, Dept Comp Engn, Ankara, Turkey", 
        "name": "Cakici, Ruket"
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        "affiliation": "Hacettepe Univ, Dept Comp Engn, Ankara, Turkey", 
        "name": "Erdem, Aykut"
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        "affiliation": "Hacettepe Univ, Dept Comp Engn, Ankara, Turkey", 
        "name": "Erdem, Erkut"
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        "affiliation": "Hacettepe Univ, Dept Comp Engn, Ankara, Turkey", 
        "name": "Ikizler-Cinbis, Nazli"
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    "description": "In the past few years, automatically generating descriptions for images has attracted a lot of attention in computer vision and natural language processing research. Among the existing approaches, data-driven methods have been proven to be highly effective. These methods compare the given image against a large set of training images to determine a set of relevant images, then generate a description using the associated captions. In this study, the authors propose to integrate an object-based semantic image representation into a deep features-based retrieval framework to select the relevant images. Moreover, they present a novel phrase selection paradigm and a sentence generation model which depends on a joint analysis of salient regions in the input and retrieved images within a clustering framework. The authors demonstrate the effectiveness of their proposed approach on Flickr8K and Flickr30K benchmark datasets and show that their model gives highly competitive results compared with the state-of-the-art models.", 
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      "issue": "6", 
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