Image Caption Generation Using Recurrent Convolutional Neural Network

Authors

  • BV Subba Rao Professor, Department of Information Technology, PVP Siddhartha Institute of Technology, Vijayawada, Andhra Pradesh, India
  • K. Meenakshi Professor, Department of Mathematics, VTU(RC), CMR Institute of Technology, Bengaluru, Karnataka, India
  • K. Kalaiarasi Assistant Professor, PG and Research Department of Mathematics, Cauvery College for Women (Autonomous), Tiruchirappalli, Tamil Nadu, India.
  • Ramesh Babu P. Associate Professor, Department of Computer Science, College of Engineering and Technology, Wollega University, Nekemte, Oromia Region, Ethiopia
  • J. Kavitha Associate Professor, Department of Basic Sciences, Cambridge Institute of Technology (CIT), Bengaluru, India.
  • V. Saravanan Associate Professor, Department of Computer Science, College of Engineering and Technology, Dambi Dollo University, Dambi Dollo, Oromia Region, Ethiopia.

Keywords:

Image Captioning, Recurrent neural network, convolutional layers

Abstract

This paper presents a residual learning (RL) approach to generate automated captions for any given image. In this approach, a convolutional neural network (CNN) is employed to extract the spectral and spatial characteristics of the image, which is essential to solve the caption generation problem, which necessitates the use of CNN. In addition to this, we consider the nuanced quality of language by incorporating an image annotation generator into the system that has been recommended. The results of the experiments that have been presented here provide convincing evidence that the developed model is an improvement upon the various approaches to image captioning that are currently being used.

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References

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Published

05.12.2023

How to Cite

Rao, B. S. ., Meenakshi, K. ., Kalaiarasi, K. ., Babu P., R. ., Kavitha, J. ., & Saravanan, V. . (2023). Image Caption Generation Using Recurrent Convolutional Neural Network . International Journal of Intelligent Systems and Applications in Engineering, 12(7s), 76–80. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/4033

Issue

Vol. 12 No. 7s (2024)

Section

Research Article

License

Copyright (c) 2023 BV Subba Rao, K. Meenakshi, K. Kalaiarasi, Ramesh Babu P., J. Kavitha, V. Saravanan

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