Abstract: Video captioning is the heuristic and most essential task in the current world to save time by converting long and highly content-rich videos into simple and readable reports in text form. It is narrating the events happening in videos in natural language sentences. It makes the way to many more interesting tasks by the use of labels, tags, and terms such as video content retrieval, video search, video tagging, etc. Video captioning is currently being attempted by many researchers using some exciting Deep learning techniques. But this approach is to find the best of machine learning for the process of captioning videos in a different way. The novel part of the proposed approach is classifying videos by using the labels existing in video frames that belong to the various categories and producing consecutive Multi-Level captions that describe the entire video in a round-robin way. Informative features are extracted from the video frames such as Gray Level Co-occurrence Matrix (GLCM) features, Hu moments, and Statistical features to provide optimal results. This model is designed with two superior and optimal classifiers such as Support Vector Machine (SVM) and Naive Bayes separately. The models are demonstrated with the prevailing standard dataset Microsoft Research Video Description corpus (MSVD) and evaluated by the benchmark classification metrics such as Accuracy, Precision, Recall, and F1-Score.

J. Vaishnavi and V. Narmatha, “Multi-level Video Captioning based on Label Classification using Machine Learning Techniques” International Journal of Advanced Computer Science and Applications(IJACSA), 13(11), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0131167

@article{Vaishnavi2022,
title = {Multi-level Video Captioning based on Label Classification using Machine Learning Techniques},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0131167},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0131167},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {11},
author = {J. Vaishnavi and V. Narmatha}
}