Year: 2023
Type: article
Abstract: Image captioning is an interesting and challenging task with applications in diverse domains such as image retrieval, organizing and locating images of users’ interest etc. It has huge potential for replacing manual caption generation for images and is especially suitable for large scale image data. Recently, deep neural network based methods have achieved great success in the field of computer vision, machine translation and language generation. In this paper, we propose an encoder-decoder based model that is capable of generating grammatically correct captions for images. This model makes use of VGG16 Hybrid Places 1365 as encoder and LSTM as decoder. To ensure the complete ground truth accuracy, the model is trained on the labelled Flickr8k and MSCOCO Captions datasets. Further, the model is evaluated using all standard metrics such as BLEU, METEOR, GLEU and ROUGE L. Experimental results indicate that the proposed model obtained a BLEU-1 score 0.6666, METEOR score 0.5060 and GLEU score 0.2469 on Flickr8k dataset and BLEU-1 score 0.7350, METEOR score 0.4768 and GLEU score 0.2798 on the MSCOCO Captions dataset. Thus, the proposed method achieved a significant performance as compared to the state-of-art approaches. To evaluate the efficacy of the model further, we also show the results of a caption generation from live sample images that reinforces the validity of the proposed approach.
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4378983079