  Texture analysis and classification are some of the problems which have been
paid much attention by image processing scientists since late 80s. If texture
analysis is done accurately, it can be used in many cases such as object
tracking, visual pattern recognition, and face recognition.Since now, so many
methods are offered to solve this problem. Against their technical differences,
all of them used same popular databases to evaluate their performance such
asBrodatz or Outex, which may be made their performance biased on these
databases. In this paper, an approach is proposed to collect more efficient
databases of texture images. The proposed approach is included two stages. The
first one is developing feature representation based on gray tone difference
matrixes and local binary patterns features and the next one is consisted an
innovative algorithm which is based on K-means clustering to collect images
based on evaluated features. In order to evaluate the performance of the
proposed approach, a texture database is collected and fisher rate is computed
for collected one and well known databases. Also, texture classification is
evaluated based on offered feature extraction and the accuracy is compared by
some state of the art texture classification methods.
