A Texture Fuzzy Classifier Based on the Training Set Clustering by a Self-Organizing Neural Network; Communications in Computer and Information Science; Vol. 542 : Analysis of Images, Social Networks and Texts
| Parent link: | Communications in Computer and Information Science Vol. 542 : Analysis of Images, Social Networks and Texts.— 2015.— [P. 187-195] |
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| Další autoři: | , |
| Shrnutí: | Title screen The paper presents a fuzzy approach to the texture classification. According to the classifier the texture class is represented as a set of clusters in N-dimensional feature space that allows generating a cluster or clusters with an arbitrary shape and precisely reflecting any group of the vectors connected with the class. For each texture class it configures the self-organizing features map and estimates a degree of the overlap of the neighboring classes. Upon matching the maps each of them creates a set of fuzzy rules reflecting the feature value statistical distribution in its clusters. Advantages of the system are simplicity of the structure generation, functioning and performance. The suggested classification technique is universal and can be used not only as a texture analyzer but independently for many other real-world classification tasks. Режим доступа: по договору с организацией-держателем ресурса |
| Jazyk: | angličtina |
| Vydáno: |
2015
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| On-line přístup: | http://dx.doi.org/10.1007/978-3-319-26123-2_18 |
| Médium: | Elektronický zdroj Kapitola |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=647733 |
| Shrnutí: | Title screen The paper presents a fuzzy approach to the texture classification. According to the classifier the texture class is represented as a set of clusters in N-dimensional feature space that allows generating a cluster or clusters with an arbitrary shape and precisely reflecting any group of the vectors connected with the class. For each texture class it configures the self-organizing features map and estimates a degree of the overlap of the neighboring classes. Upon matching the maps each of them creates a set of fuzzy rules reflecting the feature value statistical distribution in its clusters. Advantages of the system are simplicity of the structure generation, functioning and performance. The suggested classification technique is universal and can be used not only as a texture analyzer but independently for many other real-world classification tasks. Режим доступа: по договору с организацией-держателем ресурса |
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| DOI: | 10.1007/978-3-319-26123-2_18 |