Hybrid deep neural network for Bangla automated image descriptor
Automated image to text generation is a computationally challenging computer vision task which requires sufficient comprehension of both syntactic and semantic meaning of an image to generate a meaningful description. Until recent times, it has been studied to a limited scope due to the lack of visu...
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| Main Authors: | , , , , |
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| Format: | EJournal Article |
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Universitas Ahmad Dahlan,
2020-07-12.
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| LEADER | 02958 am a22003133u 4500 | ||
|---|---|---|---|
| 001 | IJAIN_499_ijain_v6i2_p109-122 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Jishan, Md Asifuzzaman |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Mahmud, Khan Raqib |e author |
| 700 | 1 | 0 | |a Azad, Abul Kalam Al |e author |
| 700 | 1 | 0 | |a Alam, Md Shahabub |e author |
| 700 | 1 | 0 | |a Khan, Anif Minhaz |e author |
| 245 | 0 | 0 | |a Hybrid deep neural network for Bangla automated image descriptor |
| 260 | |b Universitas Ahmad Dahlan, |c 2020-07-12. | ||
| 500 | |a https://ijain.org/index.php/IJAIN/article/view/499 | ||
| 520 | |a Automated image to text generation is a computationally challenging computer vision task which requires sufficient comprehension of both syntactic and semantic meaning of an image to generate a meaningful description. Until recent times, it has been studied to a limited scope due to the lack of visual-descriptor dataset and functional models to capture intrinsic complexities involving features of an image. In this study, a novel dataset was constructed by generating Bangla textual descriptor from visual input, called Bangla Natural Language Image to Text (BNLIT), incorporating 100 classes with annotation. A deep neural network-based image captioning model was proposed to generate image description. The model employs Convolutional Neural Network (CNN) to classify the whole dataset, while Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) capture the sequential semantic representation of text-based sentences and generate pertinent description based on the modular complexities of an image. When tested on the new dataset, the model accomplishes significant enhancement of centrality execution for image semantic recovery assignment. For the experiment of that task, we implemented a hybrid image captioning model, which achieved a remarkable result for a new self-made dataset, and that task was new for the Bangladesh perspective. In brief, the model provided benchmark precision in the characteristic Bangla syntax reconstruction and comprehensive numerical analysis of the model execution results on the dataset. | ||
| 540 | |a Copyright (c) 2020 Md Asifuzzaman Jishan, Khan Raqib Mahmud, Abul Kalam Al Azad, Md Shahabub Alam, Anif Minhaz Khan | ||
| 540 | |a https://creativecommons.org/licenses/by-sa/4.0 | ||
| 546 | |a eng | ||
| 690 | |a convolutional neural network; hybrid recurrent neural network; long short-term memory; bi-directional RNN; natural language descriptors | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n International Journal of Advances in Intelligent Informatics; Vol 6, No 2 (2020): July 2020; 109-122 | |
| 786 | 0 | |n 2548-3161 | |
| 786 | 0 | |n 2442-6571 | |
| 787 | 0 | |n https://ijain.org/index.php/IJAIN/article/view/499/ijain_v6i2_p109-122 | |
| 856 | 4 | 1 | |u https://ijain.org/index.php/IJAIN/article/view/499/ijain_v6i2_p109-122 |z Get Fulltext |