Texture classification by using wavelet domain association rules

Texture is an important characteristic for analysis of many types of images that including natural scenes, remotely sensed data and biomedical modalities. Texture classification aims to assign texture labels to unknown textures, according to training samples and classification rules. In this study, mufti resolution approaches such as wavelet transform and association rules are hybridized for efficient texture classification. The wavelet domain and the intensity domain (gray scale) association rules were generated for performance comparison purposes. The performed experimental studies show the efficiency of the proposed system.