Recognizing Artistic Style of Archaeological Image Fragments Using Deep Style Extrapolation
This addresses the challenge for archaeologists in classifying mixed artifact fragments, though it appears incremental as it applies existing classification methods to a specific domain.
The paper tackles the problem of categorizing fragmented ancient artworks by artistic style using a deep-learning framework, achieving state-of-the-art results for fragments with varying styles and geometries.
Ancient artworks obtained in archaeological excavations usually suffer from a certain degree of fragmentation and physical degradation. Often, fragments of multiple artifacts from different periods or artistic styles could be found on the same site. With each fragment containing only partial information about its source, and pieces from different objects being mixed, categorizing broken artifacts based on their visual cues could be a challenging task, even for professionals. As classification is a common function of many machine learning models, the power of modern architectures can be harnessed for efficient and accurate fragment classification. In this work, we present a generalized deep-learning framework for predicting the artistic style of image fragments, achieving state-of-the-art results for pieces with varying styles and geometries.