aspg-3-3928-1791416916
20261007234836
American Scientific Publishing Group
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American Scientific Publishing Group
Fusion: Practice and Applications
FPA
2770-0070
2692-4048
2026
21
1
Image Tag Generation Based on Deep Features Using Deep Learning Techniques
Heba Adnan
Raheem
Department Computer Science, College of Computer Science and Information Technology, University of Kerbala, Kerbala, Iraq
Hiba Jabbar
Aleqabie
Department Artificial Intelligence Engineering, College of Information Technology Engineering, Al-Zahraa University for Women, Kerbala, Iraq
Ameer Sameer Hamood Mohammed
Ali
Presidency of the University of Babylon, University of Babylon TOEFL Center, Babylon, Iraq
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The task of automatically generating descriptive and accurate image tags has gained significant attention in recent years due to the exponential growth of image data. Traditional methods for image tagging rely on manual annotation, which is time-consuming and subjective. Automated imagine description fills the gap between visual content and human comprehension, making it vital for activities such as information retrieval, editing, and accessibility. The expanding number of unannotated photographs makes manual tagging impossible. This paper provides a deep learning-based system that combines CNNs for feature extraction, RNNs for caption production, and attention techniques to focus on significant image areas. The model uses a sequence-to-sequence architecture to create coherent captions using pre-trained CNN features and attention-enhanced RNNs. Experiments on datasets such as Flickr8k and Flickr30k show higher performance, as evidenced by BLEU, ROUGE, and CIDEr measures. This approach provides a scalable, cutting-edge solution for image captioning, with potential applications in video analysis, enriched language production, and larger datasets.
2026
165
175
3928
https://creativecommons.org/licenses/by/4.0/
10.54216/FPA.210112
https://www.americaspg.com/journal/3/article/3928
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https://www.americaspg.com/storage/51752940541.pdf