Following publication of the original article [1], the authors reported that the Availability of data and materials section required updating. The updated text reads as follows.
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| http://dx.doi.org/10.1186/s13059-019-1781-5 | DOI Listing |
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Considering the substantial inaccuracies inherent in the traditional manual identification of ceramic categories and the issues associated with analyzing ceramics based on chemical or spectral features, which may lead to the destruction of ceramics, this paper introduces a novel provenance classification of archaeological ceramics which relies on microscopic features and an ensemble deep learning model, overcoming the time consuming and require costly equipment limitations of current standard methods, and without compromising the structural integrity and artistic value of ceramics. The proposed model includes the following: the construction of a dataset for ancient ceramic microscopic images, image preprocessing methods based on Gamma correction and CLAHE equalization algorithms, extraction of image features based on three deep learning architectures-VGG-16, Inception-v3 and GoogLeNet, and optimal fusion. This latter is based on stochastic gradient descent (SGD) algorithm, which allows optimal fitting of the fusion model parameters by freezing and unfreezing model layers.
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