Back to Search
Start Over
Semi-supervised Convolutional Triplet Neural Networks for Assessing Paper Texture Similarity
- Source :
- ACSSC
- Publication Year :
- 2020
- Publisher :
- IEEE, 2020.
-
Abstract
- 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.
- Subjects :
- Artificial neural network
Computer science
business.industry
Deep learning
010401 analytical chemistry
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
020206 networking & telecommunications
Context (language use)
Image processing
Pattern recognition
02 engineering and technology
01 natural sciences
Convolutional neural network
0104 chemical sciences
Image texture
Similarity (network science)
Feature (computer vision)
Computer Science::Computer Vision and Pattern Recognition
0202 electrical engineering, electronic engineering, information engineering
Artificial intelligence
business
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2020 54th Asilomar Conference on Signals, Systems, and Computers
- Accession number :
- edsair.doi...........2fea12abcc660dfb405849f8d804b18a