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

Innovative Texture Database Collecting Approach and Feature Extraction  Method based on Combination of Gray Tone Difference Matrixes, Local Binary  Patterns,and K-means Clustering