Abstract
This paper presents an unique approach for extracting the features of an image using a ResNet50 Convolutional Neural Network architecture and to predict the caption of the image using a novel ensemble of GRU (Gated Recurrent Units) and Recurrent Neural Network algorithms namely Long Short-Term Memory (LSTM). This approach aims to combine the faster training and performance from GRU and greater volatility throughout its gradient descent from LSTM. This technique has been implemented assuring the major factors being capacity, robustness, and visual quality. The proposed method for image captioning has been evaluated using metrics such as BLEU, CIDEr, METEOR and it outperforms when compared to conventional systems.
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Agilandeeswari, L., Sharma, K., Srivastava, S. (2022). Intelligent Image Captioning Approach with Novel Ensembled Recurrent Neural Network Model. In: Abraham, A., Gandhi, N., Hanne, T., Hong, TP., Nogueira Rios, T., Ding, W. (eds) Intelligent Systems Design and Applications. ISDA 2021. Lecture Notes in Networks and Systems, vol 418. Springer, Cham. https://doi.org/10.1007/978-3-030-96308-8_114
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DOI: https://doi.org/10.1007/978-3-030-96308-8_114
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