Efficient Feature Extraction Methods for Enhancing Analysis of Lecture Videos

Feature extraction informs fundamental computer vision and pattern recognition research. Examines how to obtain useful features from raw datasets, such as images or video frames. This stage is crucial in identifying images, recognizing objects, and analyzing frames. Effortless and accurate feature extraction may enhance machine learning models when there is a lot of visual input. Video lectures with slides, text, and diagrams can benefit from feature extraction. A study will compare video lecture analysis feature extraction algorithms and identify research gaps. We will use our survey methods to compare the performance of seven representative feature extraction algorithms: Shape Local Binary Texture (SLBT), Local Optimal Oriented Pattern (LOOP), Local Vector Pattern (LVP), Scale-Invariant Feature Transform (SIFT), Speeded-Up Robust Features (SURF), Gabor Filters, and Local Binary Patterns (LBP). SLBT and LOOP have the highest accuracy (90%), while LBP holds the lowest (85%). SIFT and SURF are widely scalable but slower to compute. Gabor and LBP filters are quick, but inaccurate and scalable. The survey found that SLBT, LOOP, and LVP are better feature extraction methods for video educational processing than others.