Open AccessIMAGE CLASSIFICATION BY PATTERN AND STRUCTURE FEATURES CLUSTERING
Author(s) -
Roman Melnyk,
Ruslan Tushnytskyy
Publication year - 2014
Publication title -
computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.184
H-Index - 11
eISSN - 2312-5381
pISSN - 1727-6209
DOI - 10.47839/ijc.8.3.685
Subject(s) - cluster analysis , hierarchical clustering , pattern recognition (psychology) , hierarchical clustering of networks , computer science , artificial intelligence , image (mathematics) , data mining , decomposition , mathematics , correlation clustering , cure data clustering algorithm , ecology , biology
An approach for decomposition of visual images by clustering and pattern classification by structure features is considered. Multilevel hierarchical clusters such as rectangles, closed regions and integrated areas are proposed. Hierarchically constructed fragments are material to form pattern structure features. To reduce the clustering algorithm complexity the tolerance coefficient and quality criteria for merging process are proposed. The results of pattern classification by structure features for some image groups by hand and automatic regimes are presented in the article. Hierarchical trees are got for different number of structure coefficients as well as for absolute and relative merging functions.