To describe the content of image: The view from image captioning

Research Article
Open access

To describe the content of image: The view from image captioning

Xiaohan Hou 1*
  • 1 Faculty of Math, Mathematics Studies, University of Waterloo, Waterloo, N2L 3G1    
  • *corresponding author x39hou@uwaterloo.ca
ACE Vol.5
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-57-7
ISBN (Online): 978-1-915371-58-4

Abstract

The aim of developing the technology of "image captioning," which integrates natural language and computer processing, is to automatically give descriptions for photographs by the machine itself. The work can be separated into two parts, which depends on correctly comprehending both language and images from a semantic and syntactic perspective. In light of the growing body of information on the subject, it is getting harder to stay abreast of the most recent advancements in the area of image captioning. Nevertheless, the review papers that are now available don't go into enough detail about those findings. The approaches, benchmarks, datasets, and assessment metrics currently in use for picture captioning are reviewed in this work. The majority of the field's ongoing study is concentrated on robust learning-based techniques, where deep reinforcement, adversarial learning, and attention processes all seem to be at the heart of this research area. Image captioning entails a brand-new field in research on computer vision. Generating a comprehensive natural language description for the source images is the fundamental issue of image captioning. This essay explores and evaluates earlier work on image captioning. Image captioning's application and task situations are introduced. The merits and disadvantages of each approach are explored after the analysis of the image captioning algorithms based on encoder-decoder and template structure. The assessment and baseline dataset for picture captioning are therefore shown. Ultimately, prospects for image captioning's progress are presented.

Keywords:

Image captioning, semantic perspective, syntactic perspective, computer processing

Hou,X. (2023). To describe the content of image: The view from image captioning. Applied and Computational Engineering,5,1-10.
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References

[1]. Kulkarni, G., Premraj, V., Ordonez, V., Dhar, S., Li, S., Choi, Y., & Berg, T. L. (2013). Babytalk: Understanding and generating simple image descriptions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(12): 2891-2903.

[2]. Herdade, S., Kappeler, A., Boakye, K., & Soares, J. (2019). Image captioning: Transforming objects into words. Advances in Neural Information Processing Systems, 32.

[3]. Wang, C., Zhou, Z., & Xu, L. (2021). An integrative review of image captioning research. In the journal of physics: conference series (Vol. 1748, No. 4, p. 042060). IOP Publishing.

[4]. Hossain, M. Z., Sohel, F., Shiratuddin, M. F., & Laga, H. (2019). A comprehensive survey of deep learning for image captioning. ACM Computing Surveys (CsUR), 51(6), 1-36.

[5]. Elhagry, A., & Kadaoui, K. (2021). A thorough review of recent deep learning methodologies for image captioning. arXiv preprint arXiv:2107.13114

[6]. Chenyu, C. (2020). Understanding Image Caption Algorithms: A Review. In Journal of Physics: Conference Series (Vol. 1438, No. 1, p. 012025). IOP Publishing.

[7]. Kim, H., Tang, Z., & Bansal, M. (2020). Dense-Caption Matching and Frame-Selection Gating for Temporal Localization in VideoQA. arXiv preprint arXiv:2005.06409.

[8]. Staniūtė, R., & Šešok, D. (2019). A systematic literature review on image captioning. Applied Sciences, 9(10), 2024.

[9]. Karpathy, A., & Fei-Fei, L. (2015). Deep visual-semantic alignments for generating image descriptions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3128-3137

[10]. Lee, S., & Kim, I. (2018). Multimodal feature learning for video captioning. Mathematical Problems in Engineering, 2018.

[11]. Rennie, S. J., Marcheret, E., Mroueh, Y., Ross, J., & Goel, V. (2017). Self-critical sequence training for image captioning. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 7008-7024).

[12]. Yao, T., Pan, Y., Li, Y., & Mei, T. (2018). Exploring visual relationship for image captioning. In Proceedings of the European conference on computer vision (ECCV) (pp. 684-699).

[13]. Anderson, P., Fernando, B., Johnson, M., & Gould, S. (2016, October). Spice: Semantic propositional image caption evaluation. In European conference on computer vision (pp. 382-398). Springer, Cham

Cite this article

Hou,X. (2023). To describe the content of image: The view from image captioning. Applied and Computational Engineering,5,1-10.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

ISBN：978-1-915371-57-7(Print) / 978-1-915371-58-4(Online)
Editor：Omer Burak Istanbullu
Conference website: http://www.confspml.org
Conference date: 25 February 2023
Series: Applied and Computational Engineering
Volume number: Vol.5
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Kulkarni, G., Premraj, V., Ordonez, V., Dhar, S., Li, S., Choi, Y., & Berg, T. L. (2013). Babytalk: Understanding and generating simple image descriptions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(12): 2891-2903.

[2]. Herdade, S., Kappeler, A., Boakye, K., & Soares, J. (2019). Image captioning: Transforming objects into words. Advances in Neural Information Processing Systems, 32.

[3]. Wang, C., Zhou, Z., & Xu, L. (2021). An integrative review of image captioning research. In the journal of physics: conference series (Vol. 1748, No. 4, p. 042060). IOP Publishing.

[4]. Hossain, M. Z., Sohel, F., Shiratuddin, M. F., & Laga, H. (2019). A comprehensive survey of deep learning for image captioning. ACM Computing Surveys (CsUR), 51(6), 1-36.

[5]. Elhagry, A., & Kadaoui, K. (2021). A thorough review of recent deep learning methodologies for image captioning. arXiv preprint arXiv:2107.13114

[6]. Chenyu, C. (2020). Understanding Image Caption Algorithms: A Review. In Journal of Physics: Conference Series (Vol. 1438, No. 1, p. 012025). IOP Publishing.

[7]. Kim, H., Tang, Z., & Bansal, M. (2020). Dense-Caption Matching and Frame-Selection Gating for Temporal Localization in VideoQA. arXiv preprint arXiv:2005.06409.

[8]. Staniūtė, R., & Šešok, D. (2019). A systematic literature review on image captioning. Applied Sciences, 9(10), 2024.

[9]. Karpathy, A., & Fei-Fei, L. (2015). Deep visual-semantic alignments for generating image descriptions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3128-3137

[10]. Lee, S., & Kim, I. (2018). Multimodal feature learning for video captioning. Mathematical Problems in Engineering, 2018.

[11]. Rennie, S. J., Marcheret, E., Mroueh, Y., Ross, J., & Goel, V. (2017). Self-critical sequence training for image captioning. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 7008-7024).

[12]. Yao, T., Pan, Y., Li, Y., & Mei, T. (2018). Exploring visual relationship for image captioning. In Proceedings of the European conference on computer vision (ECCV) (pp. 684-699).

[13]. Anderson, P., Fernando, B., Johnson, M., & Gould, S. (2016, October). Spice: Semantic propositional image caption evaluation. In European conference on computer vision (pp. 382-398). Springer, Cham

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