JOURNAL ARTICLE

Automatic Arabic Image Captioning using RNN-LSTM-Based Language Model and CNN

Huda Abdulrahman AlmuzainiN. TasniemHafida Benhidour

Year: 2018 Journal:   International Journal of Advanced Computer Science and Applications Vol: 9 (6)   Publisher: Science and Information Organization

Abstract

The automatic generation of correct syntaxial and semantical image captions is an essential problem in Artificial Intelligence. The existence of large image caption copra such as Flickr and MS COCO have contributed to the advance of image captioning in English. However, it is still behind for Arabic given the scarcity of image caption corpus for the Arabic language. In this work, an Arabic version that is a part of the Flickr and MS COCO caption dataset is built. Moreover, a generative merge model for Arabic image captioning based on a deep RNN-LSTM and CNN model is developed. The results of the experiments are promising and suggest that the merge model can achieve excellent results for Arabic image captioning if a larger corpus is used.

Keywords:
Closed captioning Computer science Merge (version control) Arabic Artificial intelligence Natural language processing Generative grammar Image (mathematics) Speech recognition Language model Generative model Information retrieval Linguistics

Metrics

61
Cited By
3.47
FWCI (Field Weighted Citation Impact)
49
Refs
0.92
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Multimodal Machine Learning Applications
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Advanced Image and Video Retrieval Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Natural Language Processing Techniques
Physical Sciences →  Computer Science →  Artificial Intelligence

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