Abstract:
Texture analysis, a vital component of computer vision and image processing, plays a pivotal role in fields such as
decoration and art. This study focuses on the classification of regular textures into 17 distinct wallpaper patterns based on
their symmetry operations. Utilizing computer vision techniques and a filter bank approach, we compared three methods:
Gabor filter bank, CNN-trained filters, and ImageNet pretrained filters, in conjunction with a random forest model. The results
revealed that ImageNet pretrained filters performed exceptionally well, achieving 87% accuracy in the 'wallpaper17' dataset
and 81% in the 'wallpaper04' dataset.
