Multi-modal features and correlation incorporated Naive Bayes classifier for a semantic-enriched lecture video retrieval system

With the advancement of science and technology and the development of internet sources, e-learning draws huge attention as it is capable of providing classroom lectures in the form of videos. Here, a method has been proposed for effective retrieval of the lecture videos, which employs the correlated naive Bayes (CNB) classifier. In this proposed method, Optical Character Recognition uses the tesseract classifier and GOM (Gabor Ordinal Measure) to extract the textual features and the image texture from the key frames. K-means clustering clusters the features and the classifier retrieves the relevant video. Experimentation has been done in the MATLAB and the parameters such as precision, recall, and F measure of the CNB are compared over the other methods such as K-Nearest Neighbour and naive Bayes (NB) classifiers. The CNB classifier achieves a better precision, recall, and F measure rate of 0.9366, 0.9511, and 0.9426, respectively.