A comprehensive construction of deep neural network‐based encoder–decoder framework for automatic image captioning systems

This study introduces a novel encoder–decoder framework based on deep neural networks and provides a thorough investigation into the field of automatic picture captioning systems. The suggested model uses a “long short‐term memory” decoder for word prediction and sentence construction, and a “convol...

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Vydáno v:	IET image processing Ročník 18; číslo 14; s. 4778 - 4798
Hlavní autoři:	Rahman, Md Mijanur, Uzzaman, Ashik, Sami, Sadia Islam, Khatun, Fatema, Bhuiyan, Md Al‐Amin
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Wiley 01.12.2024
Témata:	CNN Encoder Deep Learning Image Captioning Image Feature Extractor LSTM Decoder Pre‐trained VGG‐19 Model
ISSN:	1751-9659, 1751-9667
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Abstract	This study introduces a novel encoder–decoder framework based on deep neural networks and provides a thorough investigation into the field of automatic picture captioning systems. The suggested model uses a “long short‐term memory” decoder for word prediction and sentence construction, and a “convolutional neural network” as an encoder that is skilled at object recognition and spatial information retention. The long short‐term memory network functions as a sequence processor, generating a fixed‐length output vector for final predictions, while the VGG‐19 model is utilized as an image feature extractor. For both training and testing, the study uses a variety of photos from open‐access datasets, such as Flickr8k, Flickr30k, and MS COCO. The Python platform is used for implementation, with Keras and TensorFlow as backends. The experimental findings, which were assessed using the “bilingual evaluation understudy” metric, demonstrate the effectiveness of the suggested methodology in automatically captioning images. By addressing spatial relationships in images and producing logical, contextually relevant captions, the paper advances image captioning technology. Insightful ideas for future study directions are generated by the discussion of the difficulties faced during the experimentation phase. By establishing a strong neural network architecture for automatic picture captioning, this study creates opportunities for future advancement and improvement in the area. This article presents a comprehensive exploration into the realm of automatic image captioning systems, introducing an innovative deep neural network‐based encoder–decoder framework. The VGG‐19 model is employed as an image feature extractor, and the long short‐term memory network serves as a sequence processor, producing a fixed‐length output vector for final predictions. Diverse images sourced from open‐access datasets, including Flickr8k, Flickr30k, and MS COCO, are utilized for both training and testing.
AbstractList	Abstract This study introduces a novel encoder–decoder framework based on deep neural networks and provides a thorough investigation into the field of automatic picture captioning systems. The suggested model uses a “long short‐term memory” decoder for word prediction and sentence construction, and a “convolutional neural network” as an encoder that is skilled at object recognition and spatial information retention. The long short‐term memory network functions as a sequence processor, generating a fixed‐length output vector for final predictions, while the VGG‐19 model is utilized as an image feature extractor. For both training and testing, the study uses a variety of photos from open‐access datasets, such as Flickr8k, Flickr30k, and MS COCO. The Python platform is used for implementation, with Keras and TensorFlow as backends. The experimental findings, which were assessed using the “bilingual evaluation understudy” metric, demonstrate the effectiveness of the suggested methodology in automatically captioning images. By addressing spatial relationships in images and producing logical, contextually relevant captions, the paper advances image captioning technology. Insightful ideas for future study directions are generated by the discussion of the difficulties faced during the experimentation phase. By establishing a strong neural network architecture for automatic picture captioning, this study creates opportunities for future advancement and improvement in the area. This study introduces a novel encoder–decoder framework based on deep neural networks and provides a thorough investigation into the field of automatic picture captioning systems. The suggested model uses a “long short‐term memory” decoder for word prediction and sentence construction, and a “convolutional neural network” as an encoder that is skilled at object recognition and spatial information retention. The long short‐term memory network functions as a sequence processor, generating a fixed‐length output vector for final predictions, while the VGG‐19 model is utilized as an image feature extractor. For both training and testing, the study uses a variety of photos from open‐access datasets, such as Flickr8k, Flickr30k, and MS COCO. The Python platform is used for implementation, with Keras and TensorFlow as backends. The experimental findings, which were assessed using the “bilingual evaluation understudy” metric, demonstrate the effectiveness of the suggested methodology in automatically captioning images. By addressing spatial relationships in images and producing logical, contextually relevant captions, the paper advances image captioning technology. Insightful ideas for future study directions are generated by the discussion of the difficulties faced during the experimentation phase. By establishing a strong neural network architecture for automatic picture captioning, this study creates opportunities for future advancement and improvement in the area. This study introduces a novel encoder–decoder framework based on deep neural networks and provides a thorough investigation into the field of automatic picture captioning systems. The suggested model uses a “long short‐term memory” decoder for word prediction and sentence construction, and a “convolutional neural network” as an encoder that is skilled at object recognition and spatial information retention. The long short‐term memory network functions as a sequence processor, generating a fixed‐length output vector for final predictions, while the VGG‐19 model is utilized as an image feature extractor. For both training and testing, the study uses a variety of photos from open‐access datasets, such as Flickr8k, Flickr30k, and MS COCO. The Python platform is used for implementation, with Keras and TensorFlow as backends. The experimental findings, which were assessed using the “bilingual evaluation understudy” metric, demonstrate the effectiveness of the suggested methodology in automatically captioning images. By addressing spatial relationships in images and producing logical, contextually relevant captions, the paper advances image captioning technology. Insightful ideas for future study directions are generated by the discussion of the difficulties faced during the experimentation phase. By establishing a strong neural network architecture for automatic picture captioning, this study creates opportunities for future advancement and improvement in the area. This article presents a comprehensive exploration into the realm of automatic image captioning systems, introducing an innovative deep neural network‐based encoder–decoder framework. The VGG‐19 model is employed as an image feature extractor, and the long short‐term memory network serves as a sequence processor, producing a fixed‐length output vector for final predictions. Diverse images sourced from open‐access datasets, including Flickr8k, Flickr30k, and MS COCO, are utilized for both training and testing.
Author	Khatun, Fatema Sami, Sadia Islam Rahman, Md Mijanur Uzzaman, Ashik Bhuiyan, Md Al‐Amin
Author_xml	– sequence: 1 givenname: Md Mijanur orcidid: 0000-0001-5957-6483 surname: Rahman fullname: Rahman, Md Mijanur email: mijan@jkkniu.edu.bd organization: Jatiya Kabi Kazi Nazrul Islam University, Trishal – sequence: 2 givenname: Ashik surname: Uzzaman fullname: Uzzaman, Ashik organization: Jatiya Kabi Kazi Nazrul Islam University, Trishal – sequence: 3 givenname: Sadia Islam surname: Sami fullname: Sami, Sadia Islam organization: Jatiya Kabi Kazi Nazrul Islam University, Trishal – sequence: 4 givenname: Fatema surname: Khatun fullname: Khatun, Fatema organization: Bangabandhu Sheikh Mujibur Rahman Science & Technology University, Gopalganj – sequence: 5 givenname: Md Al‐Amin surname: Bhuiyan fullname: Bhuiyan, Md Al‐Amin organization: King Faisal University, Hofuf
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Snippet	This study introduces a novel encoder–decoder framework based on deep neural networks and provides a thorough investigation into the field of automatic picture... Abstract This study introduces a novel encoder–decoder framework based on deep neural networks and provides a thorough investigation into the field of...
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SubjectTerms	CNN Encoder Deep Learning Image Captioning Image Feature Extractor LSTM Decoder Pre‐trained VGG‐19 Model
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Title	A comprehensive construction of deep neural network‐based encoder–decoder framework for automatic image captioning systems
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