TRimCapS: MAKİNE ÖĞRENMESİ İLE TÜRKÇE DİLİNDEKİ GÖRÜNTÜ ALT YAZILARINI SINIFLANDIRMA SİSTEMİ

Öz

Dijital medyanın yaygınlaşmasıyla görüntü ve video içeriklerinin analizi önem kazanmıştır. Ancak, Türkçe alt yazı sınıflandırması, dilin yapısal zorlukları ve sınırlı veri kümeleri nedeniyle büyük bir araştırma sorunu oluşturmaktadır. Bu sorunu ele almak için TasvirEt, Flickr30k ve MS COCO veri kümeleri birleştirilerek 114.566 görüntü ve 588.867 Türkçe alt yazı içeren ImCapTR veri kümesi oluşturulmuştur. Önerilen TRimCapS sisteminde, alt yazılar TF-IDF, CountVectorizer ve GloVe ile vektörleştirilmiş, K-Means ve Latent Dirichlet Allocation kullanılarak kategorize edilmiştir. Özellik seçimi bilgi kazancı, ki-kare, Fisher skoru, karşılıklı bilgi ve temel bileşenler analizi yöntemleriyle gerçekleştirilmiştir. Çeşitli makine öğrenimi ve derin öğrenme modelleriyle yapılan sınıflandırma deneylerinde, CountVectorizer ve BERT kombinasyonu %98,84 doğruluk oranı ile en iyi sonucu vermiştir. Bilgi kazancı ve temel bileşenler analizi, diğer yöntemlere göre daha yüksek performans göstermiştir. Bu çalışma, Türkçe alt yazı sınıflandırması konusunda en kapsamlı deney sonuçlarını sunan ve oluşturulan veri kümesini araştırmacıların erişimine açan ilk çalışmadır.

Anahtar Kelimeler

• Türkçe Görüntü Altyazılandırma
• Altyazı Sınıflandırma
• BERT
• Metin Vektörleştirme
• Özellik Seçimi

TRimCapS: A MACHINE LEARNING-BASED SYSTEM FOR CLASSIFYING IMAGE CAPTIONS IN THE TURKISH LANGUAGE

Öz

With the widespread adoption of digital media, the analysis of image and video content has gained significance. However, Turkish subtitle classification presents a major research challenge due to the structural complexities of the language and the limited availability of datasets. To address this issue, the TasvirEt, Flickr30k, and MS COCO datasets were combined to create the ImCapTR dataset, which contains 114.566 images and 588.867 Turkish subtitles. In the proposed TRimCapS system, subtitles were vectorized using TF-IDF, CountVectorizer, and GloVe, and categorized using K-Means and Latent Dirichlet Allocation. Feature selection was performed using information gain, chi-square, Fisher score, mutual information, and principal component analysis. Classification experiments utilizing various machine learning and deep learning models demonstrated that the combination of CountVectorizer and BERT achieved the highest accuracy of 98.84%. Information gain and principal component analysis outperformed other feature selection methods. This study is the first to provide the most comprehensive experimental results on Turkish subtitle classification while making the constructed dataset publicly accessible to researchers.

Anahtar Kelimeler

• Turkish Image Captioning
• Caption Classification
• BERT
• Text Vectorization
• Feature Selection.

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