Digitization of museum collections is currently a major challenge faced by culturage heritage and natural history museums. Museums are expected to digitize the collections to improve not only the documentation of artefacts, but also their availability for research, reconstruction and outreach activities, and to make these digital representations available online. After an artefact has been digitized, it needs to be classified in order to enable the creation of new online services. Classification of raw, "point cloud", data according to a pre-defined typology is an open problem, and I argue that machine learning techniques offer a promising approach to solving it. When scanning a large number of artefacts (current targets are typically several thousand each day), it becomes impractical to manually input the metadata for each 3D-model. Therefore, classification software can help to generate metadata according to a pre-defined typology, to enable later retrieval of the digital files.
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A machine learning application to classify 3D models of archeological pottery. See: http://www.kultur-und-stress.de/maschinelles-lernen/using-neural-networks-to-classify-3d-scans-of-european-archaeological-pottery/
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A machine learning application to classify 3D models of archeological pottery. See: http://www.kultur-und-stress.de/maschinelles-lernen/using-neural-networks-to-classify-3d-scans-of-european-archaeological-pottery/
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