A comparative study on large-size video indexing

Abstract

Video plays an important role in our daily life. But in most video websites such as YouTube, it is always a problem to classify millions of videos that are updated every day. So there is an urgent need to develop a classification algorithm to accurately assign labels to those videos. In this paper, we use Google Cloud Platform as our calculating environment and choose the new and improved YT-8M V2 as dataset. Based on these, we compare the estimation of distribution algorithm and the recurrent neural network algorithm, trace their accuracy, and finally find the more suitable one for this problem.