  Texture analysis plays an important role in many image processing
applications to describe the image content or objects. On the other hand,
visual surface defect detection is a highly research field in the computer
vision. Surface defect refers to abnormalities in the texture of the surface.
So, in this paper a dual purpose benchmark dataset is proposed for texture
image analysis and surface defect detection titled stone texture image (STI
dataset). The proposed benchmark dataset consist of 4 different class of stone
texture images. The proposed benchmark dataset have some unique properties to
make it very near to real applications. Local rotation, different zoom rates,
unbalanced classes, variation of textures in size are some properties of the
proposed dataset. In the result part, some descriptors are applied on this
dataset to evaluate the proposed STI dataset in comparison with other
state-of-the-art datasets.
