Akademik Veri Yönetim Sistemi
Multimedia Evaluation Benchmark Workshop 2020, MediaEval 2020, Virtual, Online, 14 - 15 Aralık 2020, cilt.2882
© 2020 Copyright 2020 for this paper by its authors. All Rights Reserved.In this paper, we present the methods of approach and the main results from the Essex NLIP Team's participation in the MediEval 2020 Predicting Media Memorability task. The task requires participants to build systems that can predict short-term and long-term memorability scores on real-world video samples provided. The focus of our approach is on the use of colour-based visual features as well as the use of the video annotation meta-data. In addition, hyper-parameter tuning was explored. Besides the simplicity of the methodology, our approach achieves competitive results. We investigated the use of different visual features. We assessed the performance of memorability scores through various regression models where Random Forest regression is our final model, to predict the memorability of videos.