  Human visual brain use three main component such as color, texture and shape
to detect or identify environment and objects. Hence, texture analysis has been
paid much attention by scientific researchers in last two decades. Texture
features can be used in many different applications in commuter vision or
machine learning problems. Since now, many different approaches have been
proposed to classify textures. Most of them consider the classification
accuracy as the main challenge that should be improved. In this article, a new
approach is proposed based on combination of two efficient texture descriptor,
co-occurrence matrix and local ternary patterns (LTP). First of all, basic
local binary pattern and LTP are performed to extract local textural
information. Next, a subset of statistical features is extracted from
gray-level co-occurrence matrixes. Finally, concatenated features are used to
train classifiers. The performance is evaluated on Brodatz benchmark dataset in
terms of accuracy. Experimental results show that proposed approach provide
higher classification rate in comparison with some state-of-the-art approaches.
