A Comparative Study of Classifiers for Thumbnail Selection

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@InProceedings{Pretorious:2020:IJCNN,

• author = "Kyle Pretorious and Nelishia Pillay",
• title = "A Comparative Study of Classifiers for Thumbnail Selection",
• booktitle = "2020 International Joint Conference on Neural Networks (IJCNN)",
• year = "2020",
• abstract = "As we move into the fourth industrial revolution video streaming platforms like Netflix are turning to machine learning techniques to maintain a competitive edge in the market. Various problems such as clip creation, network optimization, customer churn prediction, amongst others, have been solved for video streaming platforms using machine learning. This paper focuses on automatic thumbnail selection for movies and series. Classifiers are used to automate the thumbnail selection. The research firstly compares the performance of different convolutional neural networks (CNNs), namely, VGG-19, Inception-v3 and ResNet-50, for solving this problem. The performance of two classifiers, namely, the best performing convolutional neural network and a hybrid approach combining a CNN and genetic programming, are compared for thumbnail selection. The CNN is used for feature extraction and genetic programming for classification. The ResNet-50 CNN outperformed the other CNNs. Both classifiers were successful for thumbnail selection with the convolutional neural network outperforming the hybrid classifier.",
• keywords = "genetic algorithms, genetic programming, Streaming media, Convolutional neural networks, Feature extraction, YouTube, Motion pictures, automatic thumbnail selection, convolutional neural networks",
• DOI = "doi:10.1109/IJCNN48605.2020.9206951",
• ISSN = "2161-4407",
• month = jul,
• notes = "Also known as \cite{9206951}",

}

Genetic Programming entries for Kyle Pretorious Nelishia Pillay

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