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Dergiler / Avrupa Bilim ve Teknoloji Dergisi / 2023 / Sayı: 52

Görüntü Altyazılama için Üst Düzey Görsel Özniteliklerin Birleştirilmesi

Fusion of High-Level Visual Attributes for Image Captioning

Murat Kılcı(IZMIR KATIP CELEBI UNIVERSITY)0009-0000-3192-1601
Özkan Çaylı(IZMIR KATIP CELEBI UNIVERSITY)0000-0002-3389-3867
Volkan Kılıç(IZMIR KATIP CELEBI UNIVERSITY)0000-0002-3164-1981
Sayfa
161–168
Yayın Tarihi
2023-12-15
DOI
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Yapay Zeka Hızlı Bakış & Çıkarımlar

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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

VizWiz ve MSCOCO Captions gibi yaygın olarak kullanılan veri kümelerinde yapılan deneysel değerlendirmeler, görsel özniteliklerin birleşiminin altyazı üretiminin doğruluğunu artırdığını göstermektedir.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Görüntü altyazılama, bir görüntünün içeriğini doğru olarak ileten bir doğal dil açıklaması üretmeyi amaçlar.
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Temel Bulgular & Veri:Ancak, optimal performansın sağlanması, gereksiz ve yanıltıcı bilgilerin işlenmesinin önlenmesi açısından bu görsel özniteliklerin değerlendirilmesi önemlidir.
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Sonuç & Katkı:VizWiz ve MSCOCO Captions gibi yaygın olarak kullanılan veri kümelerinde yapılan deneysel değerlendirmeler, görsel özniteliklerin birleşiminin altyazı üretiminin doğruluğunu artırdığını göstermektedir.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Görsel Öznitelikler · Görüntü Altyazılama · Android Uygulama

Özet

Görüntü altyazılama, bir görüntünün içeriğini doğru olarak ileten bir doğal dil açıklaması üretmeyi amaçlar. Son zamanlarda, altyazıların doğruluğunu arttırmak için görsel öznitelikleri çıkaran derin öğrenme modelleri kullanılmaktadır. Ancak, optimal performansın sağlanması, gereksiz ve yanıltıcı bilgilerin işlenmesinin önlenmesi açısından bu görsel özniteliklerin değerlendirilmesi önemlidir. Bu çalışmada, anlamsal bölümleme, nesne algılama, örnek bölümleme, anahtar nokta algılamanın ve bunların birleşiminin görsel öznitelikleri kullanıyoruz. VizWiz ve MSCOCO Captions gibi yaygın olarak kullanılan veri kümelerinde yapılan deneysel değerlendirmeler, görsel özniteliklerin birleşiminin altyazı üretiminin doğruluğunu artırdığını göstermektedir. Ayrıca, görsel özniteliklerin birleşimini kullanan görüntü altyazılama modeli, NObstacle adını verdiğimiz özel tasarlanmış Android uygulamamıza entegre edilerek internet bağlantısı gerektirmeden altyazı üretimini sağlamaktadır.

Abstract

Image captioning aims to generate a natural language description that accurately conveys the content of an image. Recently, deep learning models have been used to extract visual attributes from images, enhancing the accuracy of captions. However, it is essential to assess these visual attributes to ensure optimal performance and avoid incorporating redundant or misleading information. In this study, we employ the visual attributes of semantic segmentation, object detection, instance segmentation, keypoint detection, and their fusion. Experimental evaluations on the commonly used datasets VizWiz and MSCOCO Captions demonstrate that the fusion of visual attributes improves the accuracy of caption generation. Furthermore, the image captioning model, which utilizes the fusion of visual attributes, has been embedded into our custom-designed Android application, named NObstacle, enabling captioning without the need for an internet connection.

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Bu Makaleye Atıf Yap

Kılcı, M., Çaylı, Ö., Kılıç, V. (2023). Görüntü Altyazılama için Üst Düzey Görsel Özniteliklerin Birleştirilmesi. Avrupa Bilim ve Teknoloji Dergisi, Sayı: 52, 161–168. https://ajindex.com/articles/1368642

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