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Home > Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 35 > No. 17: IAAI-21, EAAI-21, AAAI-21 Special Programs and Special Track

Computational Visual Ceramicology: Matching Image Outlines to Catalog Sketches

February 1, 2023

Authors

Barak Itkin

Tel Aviv University, Israel


Lior Wolf

Tel Aviv University, Israel


Nachum Dershowitz

Tel Aviv University, Israel


Proceedings:

No. 17: IAAI-21, EAAI-21, AAAI-21 Special Programs and Special Track

Volume

Issue:

Proceedings of the AAAI Conference on Artificial Intelligence, 35

Track:

AAAI Special Track on AI for Social Impact

Downloads:

Download PDF

Abstract:

Field archeologists are called upon to identify potsherds, for which they rely on their professional experience and on reference works. We have developed a recognition method starting from images captured on site, which relies on the shape of the sherd's fracture outline. The method sets up a new target for deep-learning, integrating information from points along inner and outer surfaces to learn about shapes. Training the classifiers required tackling multiple challenges that arose on account of our working with real-world archeological data: paucity of labeled data; extreme imbalance between instances of different categories; and the need to avoid neglecting rare classes and to take note of minute distinguishing features of some classes. The scarcity of training data was overcome by using synthetically-produced virtual potsherds and by employing multiple data-augmentation techniques. A novel form of training loss allowed us to overcome classification problems caused by under-populated classes and inhomogeneous distribution of discriminative features.

DOI:

10.1609/aaai.v35i17.17740


AAAI

Proceedings of the AAAI Conference on Artificial Intelligence, 35



Topics: AAAI

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