NDLI: Self-organizing maps for texture classification

Content Provider	Springer Nature Link
Author	Petrov, Nedyalko Georgieva, Antoniya Jordav, Ivan
Copyright Year	2012
Abstract	A further investigation of our intelligent machine vision system for pattern recognition and texture image classification is discussed in this paper. A data set of 335 texture images is to be classified into several classes, based on their texture similarities, while no a priori human vision expert knowledge about the classes is available. Hence, unsupervised learning and self-organizing maps (SOM) neural networks are used for solving the classification problem. Nevertheless, in some of the experiments, a supervised texture analysis method is also considered for comparison purposes. Four major experiments are conducted: in the first one, classifiers are trained using all the extracted features without any statistical preprocessing; in the second simulation, the available features are normalized before being fed to a classifier; in the third experiment, the trained classifiers use linear transformations of the original features, received after preprocessing with principal component analysis; and in the last one, transforms of the features obtained after applying linear discriminant analysis are used. During the simulation, each test is performed 50 times implementing the proposed algorithm. Results from the employed unsupervised learning, after training, testing, and validation of the SOMs, are analyzed and critically compared with results from other authors.
Starting Page	1499
Ending Page	1508
Page Count	10
File Format	PDF
ISSN	09410643
Journal	Neural Computing and Applications
Volume Number	22
Issue Number	7-8
e-ISSN	14333058
Language	English
Publisher	Springer-Verlag
Publisher Date	2012-01-18
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Self-organizing maps Texture classification Feature extraction Statistical analysis PCA LDA Artificial Intelligence (incl. Robotics) Data Mining and Knowledge Discovery Probability and Statistics in Computer Science Computational Science and Engineering Image Processing and Computer Vision Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

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