Effects of Different Pre-Training Algorithms on Video Classification Results

The dataset used in the study consists of 600 videos obtained by us from social media (Instagram).The data set includes 6 different video files.These files contain videos of different subjects and such as dance, driving car, fitness, make-up, mukbang.Dataset, containing 600 videos, 100 videos in each class.Additionally, the length of each video is approximately 1000 and the number of frames varies accordingly.Additionally, the videos are in mp4 format and received and processed in this format.All frames were used in classification.Because video combines multiple picture frames to create a series of images, classification is performed on this image data.The image feature information of these frames was extracted with the help of three different pre-training algorithms.These features include all features in the framework.These algorithms; ResNet50, ResNet101, GoogleNet.It is possible to collect data with different algorithms during the pre-training phase, observe the results clearly with different algorithms, and classify them in MATLAB by taking 1000 features from each algorithm.To check whether the features obtained from each pre-training algorithm were classifiable, before proceeding to the classification stage, the videos were found to be classifiable by t-SNE and pixel analysis.After analyzing the features of three different pre-training algorithms, classification was started.In the classification phase, the Bi-LSTM classifier was used because it is a time-dependent function and classifies all the data after reviewing it in detail.In conclusion; The effects of videos belonging to these six different classes on the classification accuracy of the results obtained with the same parameters but different pre-training algorithms were investigated.