Implementing Complexity in Automatic Image Caption Generator using Recurrent Neural Network over Long Short-Term Memory

Abstract

Aim: To grasp the context of a picture and explain it in natural languages, such as English, using an image caption generator and processing ideas.

Materials and Methods: The performance analysis for the highest accuracy in picture caption generator using beam search (N=10) and long short term memory (N=10) with 70% and 30% split sizes of training and test datasets, using G-power setting parameters: (α=0.05 and power=0.86) respectively

Results: RNN has significantly better accuracy (91%) compared to long short term memory accuracy (76%) and attained the significance value of 0.670 (Twotailed, p>0.05).

Conclusion: Recurrent neural networks achieved significantly better classification than Long short-term memory for generating a description of the image.