Tagging Anglo-Saxon Stone Sculptures Using Multi-Label Image Classification ML Techniques

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• Zeynep Aki - RSE, University of Durham
• Thursday 25 April 2024, 13:00-14:00
• West 2, West Hub.

If you have a question about this talk, please contact Jack Atkinson.

This project involves developing a machine learning model to automatically classify images of Anglo Saxon Stone Sculptures based on their features, referred to as “tags”. The aim is to have a model that can accurately identify various characteristics from these sculptures, such as animals, patterns, and architectural details, in images it has not seen before.

The process begins with data preparation, where images and associated metadata are standardized to ensure uniformity and relevance. This step involves converting images to a common format, organizing them systematically, and refining the metadata to align with the model’s needs. This preparation is crucial as it directly impacts the model’s ability to learn and generalize from the training data.

Following data preparation, the project employs Convolutional Neural Networks (CNNs) for the training phase. CNNs are chosen for their effectiveness in image recognition tasks. The training involves adjusting the model to identify and learn from the patterns and features in the training dataset. This includes resizing images for consistency, specifying model architecture with layers designed for feature extraction and classification, and selecting optimization and loss functions appropriate for a multi-label classification task.

This project showcases the potential of applying advanced machine learning techniques to cultural heritage preservation, offering a novel tool for cataloging and studying historical artifacts. It illustrates how technology can aid in the detailed analysis of cultural artifacts, providing deeper insights and facilitating easier access to information about our historical heritage.

This talk is part of the RSE Seminars series.

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