Unsupervised texture image classification using self-organizing maps

Nedyalko Petrov*, Ivan Jordanov

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In this paper we report results from unsupervised texture image classification on a data set of images collected with our intelligent machine vision system for pattern recognition (assuming no a priori human vision expert knowledge is available for the image classes). The simulation of the investigated system includes four main phases: data collection and feature extraction, feature analysis, classifier training, and classifier testing and evaluation. Self-Organizing Maps (SOM) are used for classification of the collection of images into several classes, based on their features and texture characteristics. Three main experiments are conducted during this research: in the first one, all extracted features are used for training the classifiers without any statistical pre-processing of the dataset; in the second simulation, the classifiers are trained after normalization of the available data; and in the last experiment, the trained SOMs use linear transformations of the original features, received after pre-processing with principal component analysis (PCA). Each test is performed 50 times and the classification results are assessed using three commonly applied metrics, namely: accuracy rate, sensitivity and specificity. Finally, the findings of this investigation are compared with results from other authors.

Original languageEnglish
Title of host publicationProceedings of the 2011 International Conference on Artificial Intelligence, ICAI 2011
PublisherCSREA Press
Number of pages7
ISBN (Print)9781601321848
Publication statusPublished - 2011
Event2011 International Conference on Artificial Intelligence - Las Vegas, United States
Duration: 18 Jul 201121 Jul 2011


Conference2011 International Conference on Artificial Intelligence
Abbreviated titleICAI 2011
Country/TerritoryUnited States
CityLas Vegas


  • Feature extraction
  • Image analysis
  • Principal component analysis
  • Self-organizing maps
  • Texture classification
  • Unsupervised learning


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