1 May 2003 Texture-based paper characterization using nonsupervised clustering
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Proceedings Volume 5132, Sixth International Conference on Quality Control by Artificial Vision; (2003) https://doi.org/10.1117/12.515125
Event: Quality Control by Artificial Vision, 2003, Gatlinburg, TE, United States
Abstract
A non-supervised clustering based method for classifying paper according to its quality is presented. The method is simple to train, requiring minimal human involvement. The approach is based on Self-Organizing Maps and texture features that discriminate the texture of effectively. Multidimensional texture feature vectors are first extracted from paper images. The dimensionality of the data is then reduced by a Self-Organizing Map (SOM). In dimensionality reduction, the feature data are projected to a two-dimensional space and clustered according to their similarity. The clusters represent different paper qualities and can be labeled according to the quality information of the training samples. After that, it is easy to find the quality class of the inspected paper by checking where a sample is placed in the low-dimensional space. Tests based on images taken in a laboratory environment from four different paper quality classes provided very promising results. Local Binary Pattern (LBP) texture features combined with a SOM-based approach classified the test data almost perfectly: the error percentage was only 0.2% with the multiresolution version of LBP and 1.6% with the regular LBP. The improvement to the previously used texture features in paper inspection is huge: the classification error is reduced over 40 times. In addition to the excellent classification accuracy, the method also offers a self-intuitive user interface and a synthetic view to the inspected data.
© (2003) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Markus Turtinen, Matti Pietikaeinen, Olli Silven, Topi Maenpaa, Matti Niskanen, "Texture-based paper characterization using nonsupervised clustering", Proc. SPIE 5132, Sixth International Conference on Quality Control by Artificial Vision, (1 May 2003); doi: 10.1117/12.515125; https://doi.org/10.1117/12.515125
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