Research Area: Machine Learning
Auto Image captioning is defined as the process of generating captions or textual descriptions for images based on the contents of the image. It is a machine learning task that involves both natural language processing (for text generation) and computer vision (for understanding image contents). Auto image captioning is a very recent and growing research problem nowadays. Day by day various new methods are being introduced to achieve satisfactory results in this field. However, there are still lots of attention required to achieve results as good as a human. This study aims to find out in a systematic way that what different and recent methods and models are used for image captioning using deep learning? What methods are implemented to use those models? And what methods are more likely to give good results. For doing so we have performed a systematic literature review on recent studies from 2017 to 2019 from well-known databases (Scopus, Web of Sciences, IEEEXplore). We found a total of 61 prime studies relevant to the objective of this research. We found that CNN is used to understand image contents and find out objects in an image while RNN or LSTM is used for language generation. The most commonly used datasets are MS COCO used in all studies and flicker 8k and flicker 30k. The most commonly used evaluation matrix is BLEU (1 to 4) used in all studies. It is also found that LSTM with CNN has outperformed RNN with CNN. We found that the two most promising methods for implementing this model are Encoder Decoder, and attention mechanism and a combination of them can help in improving results to a good scale. This research provides a guideline and recommendation to researchers who want to contribute to auto image captioning.
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Author(s) Name: Murk Chohan , Adil Khan, Muhammad Saleem Mahar, Saif Hassan, Abdul Ghafoor ,Mehmood Khan
Journal name: International Journal of Advanced Computer Science and Applications(IJACSA)
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Publisher name: The Science and Information(SAI)
DOI: 10.14569/IJACSA.2020.0110537
Volume Information: Volume 11 Issue 5, 2020.
