Akıllı Telefonlar için Birleştirme Modeli Tabanlı Görüntü Altyazılama

Yıl 2021, Sayı: 26 - Ejosat Özel Sayı 2021 (HORA), 191 - 196, 31.07.2021

Muharrem Baran , Özge Taylan Moral , Volkan Kılıç

https://doi.org/10.31590/ejosat.950924

Cited By: 3

Öz

Görüntü altyazılama, bir görüntünün metinsel açıklamasını doğal dil işleme ve bilgisayarlı görü kullanılarak oluşturma işlemidir. Bir görüntünün görsel içeriğini makineye tanımlatmak, potansiyel uygulamaları nedeniyle son yıllarda artarak ilgi görmüştür. Bu çalışmada, akıllı telefonlarda uygulanabilir, kodlayıcı-kod çözücü yaklaşımına dayanan birleştirme modeli tabanlı bir görüntü altyazılama sistemi önerilmektedir. Önerilen birleştirme modelinde kodlayıcı olarak görüntü özniteliklerini çıkarmak için VGG16 evrişimsel sinir ağları ve kelime özelliklerini çıkarmak için uzun-kısa dönemli bellek yapısı kullanılmıştır. Bu iki işlem sonrası, görüntü özniteliklerinin ve oluşturulan kelime özelliklerinin kodlanmış biçimleri önerilen modelde birleştirilmiştir. Bu iki kodlanmış girdinin kombinasyonu daha sonra dizideki bir sonraki kelimeyi oluşturmak için çok basit bir kod çözücü modeli tarafından kullanılarak görüntülerin doğal dile uygun altyazıları başarıyla üretilmiştir. Önerilen sistem Flickr8k/30k veri kümeleri üzerinde BLEUn metriği kullanılarak test edilmiş ve literatürdeki çalışmalarla kıyaslanarak sağladığı üstünlük gösterilmiştir. Önerilen sistem, ayrıca, benzer çalışmalardan farklı olarak internet bağlantısı olmadan görüntü altyazısı üretebilecek şekilde geliştirdiğimiz ImCap adlı Android uygulamamız üzerinde de başarıyla çalıştırılmıştır. Bu uygulama ile görüntü altyazılamanın daha çok kullanıcıya ulaşması amaçlanmıştır.

Anahtar Kelimeler

Görüntü Altyazılama, Bilgisayarlı Görü, Doğal Dil işleme, Android

Merge Model Based Image Captioning for Smartphones

Öz

Image Captioning is the process of generating a textual description of an image by using both natural language processing and computer vision. Definition of the visual content of an image to the machine has attracted increasing attention in recent years due to its potential applications. In this study, an image captioning system based on an encoder-decoder merge model approach, applicable to smartphones, is proposed. In the proposed merge model, VGG16 convolutional neural networks are used to extract the image features and long-short term memory are used to extract the word features as encoder. After these two processes, the encoded forms of the images and the word features were merged in the proposed model. Image captioning was done successfully after the combination of these two encoded inputs had been used by a very simple decoder model to generate the next word in the sequence. The proposed system was tested using the BLEUn metric on the Flickr8k/30k dataset and its superiority was shown by comparing it with the studies in the literature. The proposed system was also integrated with our Android application called ImCap, which we have developed to generate captions without an internet connection, unlike other similar studies. With this application, image captioning is aimed to reach more users.

Anahtar Kelimeler

Image Captioning, Computer Vision, Natural Language Processing, Android

Kaynakça

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