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In the context of papers used in the graphic arts, including silver gelatin, inkjet, and wove papers, prior work has studied measures of texture similarity for purposes of classifying such papers. The majority of prior work has been based on classical image processing approaches such as Fourier, wavelet, and fractal analysis. In this work, recent advances in deep learning are used to develop a texture similarity approach for measuring paper texture similarity. Since the available datasets generally lack labels, the convolutional neural network is trained using triplet loss to minimize the feature distance of tiles from the same image while simultaneously maximizing the feature distance of tiles drawn from different images. The approach is tested on three paper texture image databases considered in prior works and the results suggest the proposed approach achieves state-of-the-art performance.
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This content will become publicly available on December 10, 2025
Feature Papers in Photochemistry
As the Special Issues “Feature Papers in Photochemistry” and “Feature Papers in Photochemistry II” conclude, it is crucial to acknowledge the remarkable progress and persistent gaps that continue to shape the journey of photochemistry research [...]
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- Award ID(s):
- 2403875
- PAR ID:
- 10559349
- Publisher / Repository:
- MDPI
- Date Published:
- Journal Name:
- Photochem
- Volume:
- 4
- Issue:
- 4
- ISSN:
- 2673-7256
- Page Range / eLocation ID:
- 511 to 517
- Subject(s) / Keyword(s):
- Photochemistry photocatalysis sunlight photoprotection photodamage photostability photooxidation photoreduction photosynthesis,photobiology photodecarboxylation UV visible UV-vis fluorescence luminescence spectroscopy
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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