1 Ocak 2017 Dergi makalesi Açık Erişim

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

Citation Style Language JSON

{
  "DOI": "10.1049/iet-cvi.2016.0286", 
  "abstract": "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.", 
  "author": [
    {
      "family": "Kilickaya", 
      "given": " Mert"
    }, 
    {
      "family": "Akkus", 
      "given": " Burak Kerim"
    }, 
    {
      "family": "Cakici", 
      "given": " Ruket"
    }, 
    {
      "family": "Erdem", 
      "given": " Aykut"
    }, 
    {
      "family": "Erdem", 
      "given": " Erkut"
    }, 
    {
      "family": "Ikizler-Cinbis", 
      "given": " Nazli"
    }
  ], 
  "container_title": "IET COMPUTER VISION", 
  "id": "46249", 
  "issue": "6", 
  "issued": {
    "date-parts": [
      [
        2017, 
        1, 
        1
      ]
    ]
  }, 
  "page": "398-406", 
  "title": "Data-driven image captioning via salient region discovery", 
  "type": "article-journal", 
  "volume": "11"
}