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In a previous study, we applied patch classification, a fiber estimation method, on macro-images of paper obtained using a digital camera; these macro-images were obtained from a limited number of materials. The method efficiently classified these images by dividing them into image patches. However, this method did not analyze the entire macro-image. Therefore, to extend the application of this patch-based paper fiber classification method to entire macro-images, we propose a method wherein EfficientNet is applied for fiber estimation in macro-images; the type of fiber of each image patch was estimated in two stages. The first stage was patch fiber classification 1 (PFC 1), wherein image patches were classified into three fibers, yielding PFC results for the patches comprising a macro-image. If more than 80% of the patches were determined to be of the same fiber by a majority vote, these results would be used as the paper fiber estimation (PFE) results for the macro-image. If less than 80% were classified to be of the same fiber under PFC 1, then patch fiber classification 2 (PFC 2), wherein the patches between two fibers were classified, was performed. Then, by majority, the PFC 2 results would be used as the estimation results. We targeted three fibers, namely kõzo, mitsumata, and gampi; and we used 1179 macro-images (393 for each fiber). The fiber estimation accuracy for the macro-images was evaluated using three-fold cross-validation. We achieved an accuracy of 87.6% in estimating fibers in the macro-images of paper using the proposed method.
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