Akıllı Telefonlar için Çok Katmanlı Kapılı Tekrarlayan Birim Tabanlı Video Altyazılama

Year 2021, Issue: 32, 221 - 226, 31.12.2021

Bengü Fetiler Özkan Çaylı Özge Taylan Moral Volkan Kılıç Aytuğ Onan

https://doi.org/10.31590/ejosat.1039242

Cited By: 5

Abstract

Video altyazılama, bilgisayarlı görü (CV) ve doğal dil işleme (NLP) alanlarında ilgi çeken dilbilgisel ve anlamsal olarak anlamlı tanımlar oluşturan bir görsel anlama işlemidir. Mobil platformun hesaplama gücündeki son gelişmeler, CV ve NLP tekniklerini kullanan birçok video altyazılama uygulamasının önünü açmıştır. Bu video altyazılama uygulamaları, çoğunlukla, kodlayıcı üzerinde evrişimli sinir ağları (CNN'ler) ve kod çözücü üzerinde tekrarlayan sinir ağları (RNN’ler) kullanan internet bağlantısıyla çalışan kodlayıcı-kod çözücü yaklaşımına bağlıdır. Ancak, bu yaklaşım çevrimiçi veri aktarımından dolayı doğru altyazı sonuçları ve hızlı yanıt alma açısından yeterince güçlü değildir. Bu nedenle, bu bildiride, kodlayıcı-kod çözücü yaklaşımı anlamsal olarak daha uyumlu altyazı oluşturmak için çok katmanlı kapılı tekrarlayan birim (GRU) altında diziden dizeye yaklaşımı ile genişletilmiştir. Her video karesinin görüntü özelliklerinden görsel bilgiler, altyazı oluşturma amacıyla çok katmanlı GRU tabanlı kod çözücüyü beslemek için kodlayıcıdaki ResNet-101 CNN ile çıkarılır. Önerilen yaklaşım, sekiz performans metriği altında MSVD veri kümesi üzerinde deneyler kullanılarak gelişmiş yaklaşımlarla karşılaştırılmıştır. Ayrıca, önerilen yaklaşım internet bağlantısı olmadan daha hızlı altyazı üretme yeteneğine sahip, WeCap adlı, özel tasarlanmış Android uygulamamıza gömülmüştür.

Keywords

Evrişimsel sinir ağı, Kapılı Tekrarlayan Birim, Doğal Dil İşleme, Video Altyazılama, Android Uygulama

Project Number

120N995, 2021-ÖDL-MÜMF-0006

Video Captioning Based on Multi-layer Gated Recurrent Unit for Smartphones

Abstract

Video captioning is the visual understanding process to generate grammatically and semantically meaningful descriptions that are of interest in the fields of computer vision (CV) and natural language processing (NLP). Recent advances in the computing power of the mobile platform have led to many video captioning applications that use CV and NLP techniques. These video captioning applications mainly depend on the encoder-decoder approach running with the internet connection, which employs convolutional neural networks (CNNs) on the encoder and recurrent neural networks (RNNs) on the decoder. However, this approach is not powerful enough to get accurate captioning results, and fast response due to online data transfer. In this paper, therefore, the encoder-decoder approach has been extended with a sequence-to-sequence model under a multi-layer gated recurrent unit (GRU) to generate a semantically more coherent caption. Visual information from image features of each video frame is extracted with ResNet-101 CNN in the encoder to feed the multi-layer GRU based decoder for caption generation. The proposed approach has been compared with the state-of-the-art approaches using experiments on the MSVD dataset under eight performance metrics. In addition, the proposed approach is embedded into our custom-designed Android application, called WeCap, capable of faster caption generation without an internet connection.

Keywords

Convolutional Neural Network, Gated Recurrent Units, Natural Language Processing, Video Captioning, Android Application.

Supporting Institution

TÜBİTAK, BAP

Project Number

120N995, 2021-ÖDL-MÜMF-0006

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