Year: 2024
Type: article
Abstract: Image captioning aims to detect this information by describing the image content through image and text processing techniques. Now-a-days image captioning is one of the recent and growing research problem. Day by day various solutions are being introduced for solving the problem. Even though, many solutions are already available, a lot of attention is still required for getting better and precise results. So, we came up with the idea of developing a image captioning model using different combinations of Convolutional Neural Network architecture along with Long Short Term Memory to get better results. We have used three combination of CNN and LSTM for developing the model. The proposed model is trained with three Convolutional Neural Network architecture such as Inception-v3,Xception, ResNet50 for feature extraction and Long ShortTerm Memory for generating the relevant captions. Among these models, the best combination based on the accuracy of the model. It is trained using the Flicker8k dataset
Authors: A. T. V Sai Sony, N. Sailaja, B. Roshini, B. Roshini, Mr. S. Syam Kumar
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4396808616