With rapid growth of social media platforms and widespread use of handheld devices such as mobile phones and video cameras, the number of videos being captured and shared over the internet has increased significantly. However, due to the lack of organization, most of these videos lack semantic context. Traditional methods of video retrieval involve searching for relevant videos using attached semantics. which has led to the need for content-based video retrieval, where video contents are utilized for searching, whether by video or text queries.The primary goal of our system is to provide relevant videos from a database. Our proposed approach in this paper employs Pearson’s coefficient of correlation (PCC) for key frame extraction from videos, subsequently building a feature vector that represents the video’s content. We have also experimented with linear binary pattern (LBP) and Colour moments (CM). We have used precision metric for evaluating performance. For conducting experiments, we utilized the UCF101 dataset, comprising 13,320 videos across 101 categories.