Fusion of High-Level Visual Attributes for Image Captioning

Yıl 2023, Sayı: 52, 161 - 168, 15.12.2023

Murat Kılcı , Özkan Çaylı , Volkan Kılıç

Öz

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.

Anahtar Kelimeler

Visual Attributes, Image Captioning, Android Application

Destekleyen Kurum

TUBITAK ve İKCU BAP

Proje Numarası

120N995, 2021-ÖDL-MÜMF-0006, 2022-TYL-FEBE-0012

Teşekkür

This research was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) British Council (The Newton Katip Celebi Fund Institutional Links, Turkey UK project: 120N995) and by the scientific research projects coordination unit of Izmir Katip Celebi University (project no: 2021-ÖDL-MÜMF-0006, & 2022-TYL-FEBE-0012).

Görüntü Altyazılama için Üst Düzey Görsel Özniteliklerin Birleştirilmesi

Öz

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.

Anahtar Kelimeler

Görsel Öznitelikler, Görüntü Altyazılama, Android Uygulama

Proje Numarası

120N995, 2021-ÖDL-MÜMF-0006, 2022-TYL-FEBE-0012

Kaynakça

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