Research scholar, dept. of computer science & engineering, specialty of image processing

Abstract EN

Nowadays, video recordings are widely used and easy in spreading the knowledge among the students.

Due to the rapid development of recording technologies and video-based learning, the large number of videos is published on the Internet.

The main challenge is to retrieve the appropriate video based on the user requirement.

This paper proposes the intent aware optimization based on grey wolf optimizer for retrieving the lecture video.

The extraction of keyframe is the initial step in the proposed system.

The next step is the key frame extraction in which the keywords from the key frame are recognized by the optical character recognition and LVP (Local Vector Pattern).

After the features are extracted, the PENN (Probability Extended Nearest Neighbour) classifier is utilized retrieve the relevant videos for the text or video query.

Subsequently, the user selects one video, which is used for the matching purpose based on the optimization.

The grey wolf optimizer is applied to the input database where the clustering task acquires the optimal solution.

Finally, the user selected video is matched with the optimal solution to retrieve the more relevant video for the input query.

The experimental results are validated, and the parameters used to analyze the performance are F-measure, Recall, and Precision.

The performance is compared with the existing systems using MATLAB implementation.

The higher precision value of 75% is attained by the proposed method, which ensures the efficient retrieval of content based lecture video.