SVM Based Video Content Retrieval for Online Sessions

Now days users are interested in distance learning as there is rapid growth in digital data due to day today development in information as well as computer technology. Also its applications have grate response in market. Peoples are attracted towards interactivity in each thing, we found that for e-learning is a very interactive way to learn and understand things. Now a days, YouTube is widely used for video sharing. It is having certain limitations such as, it having inactivity in online learning. In online study students expecting some extra guidelines from available resources. In this project we developed video annotation system to promote active learning. In this project, we achieved active participation of students. we are using technologies that extracts some important keywords from textual information. MOOCs model is another technology to solve interaction problem of users in active learning. Our system is interactive as it has ability to assign real-time annotations to the video. In our system user can give their active participation as they are directly interacting with our system. As part of our contribution in this project we did SVM analysis to provide recommended videos for end users. Support Vector Machine(SVM) algorithm classifies the stuff according to user interest. So, in our system user can search for video and they get recommended video list for their study.