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Open Access Transfer Learning for Data Triage Applications

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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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Document Type: Research Article

Publication date: January 1, 2018

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  • For more than 30 years, the Electronic Imaging Symposium has been serving those in the broad community - from academia and industry - who work on imaging science and digital technologies. The breadth of the Symposium covers the entire imaging science ecosystem, from capture (sensors, camera) through image processing (image quality, color and appearance) to how we and our surrogate machines see and interpret images. Applications covered include augmented reality, autonomous vehicles, machine vision, data analysis, digital and mobile photography, security, virtual reality, and human vision. IS&T began sole sponsorship of the meeting in 2016. All papers presented at EIs 20+ conferences are open access.

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