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Essex-NLIP at MediaEval Predicting MediaMemorability 2020 Task

Jacutprakart, Janadhip and Savran Kiziltepe, Rukiye and Gan, John and Papanastasiou, Giorgos and Garcia Seco De Herrera, Alba (2020) Essex-NLIP at MediaEval Predicting MediaMemorability 2020 Task. In: MediaEval 2020 Multimedia Benchmark Workshop 2020, 2020-12-14 - 2020-12-15, Online.

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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.

Item Type: Conference or Workshop Item (Paper)
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 27 Sep 2022 13:54
Last Modified: 27 Sep 2022 13:57

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