Convolutional neural networks (CNNs) have improved the field of computer vision in the past years and allow groundbreaking new and fast automatic results in various scenarios. However, the training effect of CNNs when only scarce data are available is not yet examined in detail. Transfer
learning is a technique that helps overcoming training data shortage by adapting trained models to a different but related target task. We investigate the transfer learning performance of pre-trained CNN models on variably sized training datasets for binary classification problems, which resemble
the discrimination between relevant and irrelevant content within a restricted context. This often plays a role in data triage applications such as screening seized storage devices for means of evidence. The evaluation of our work shows that even with a small number of training examples, the
models can achieve promising performances of up to 96% accuracy. We apply those transferred models to data triage by using the softmax outputs of the models to rank unseen images according to their assigned probability of relevance. This provides a tremendous advantage in many application
scenarios where large unordered datasets have to be screened for certain content.
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CONTENT-BASED IMAGE RETRIEVAL;
CONVOLUTIONAL NEURAL NETWORKS;
Document Type: Research Article
January 1, 2018
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