Abstract: This paper describes a new approach for the classification of birds types. It is based on the fact that a tested image is composed of different texture regions that can be classified depending on features of Gray-level Co-occurrence Matrices (GLCM). This approach consists of two stages, the first stage is built a data base of features for a large number of variety images using texture features, and the second step is decomposed the tested image into patterns and extracting the texture features by using the gray-level Co-occurrence matrix (GLCM) in different four directions. The obtained results for unknown bird which features calculated for it compared with patterns data base demonstrate that the existing paper in a good results for certain features using different directions.

Introduction
This paper introduces a new approach of bird s classification which is a part of image processing using Gray level co-occurrence matrix (GLCM). The simplest away practice in this paper is a classification of bird s images into patterns with the use of their textures features in different direction of GLCM matrix to match the experimental results with a feature of data base. The patterns have high variability of reflection texture charactertics. Therefore using texture features analysis methods of image is a good way to obtain accurate results.
Another direction in this paper is using a feature of data base for a large number of variety images. Patterns features of data base may be applied for realizing a wide pattern in different texture without imposing any restriction on their distribution. Based on a topicality of the given approaches this paper present the texture segmentation for a new approach by using Gray level Co-occurrence Matrix (GLCM).
Machine classification vision based on gray level co-occurrence matrix of birds classification is necessary and important method. As it lead to obtain a good results for classification in certain features characteristics. There are different approaches in computer vision, Commander K.Velu used an Intelligent Segmentation of Industrial Component Images. Virendra Pathak and Onkar Dikshit used Segment based classification of Indian urban environment. P. Tymkow, A. Borkowski introduced land cover classification using airborne laser scanning data and photographs.
When we propose a bird classification based on Gray level co-occurrence matrix (GLCM) in this paper, it is necessary to allocate following points.
a. Choice the patterns of textures attribute for a large numbers of variety Images and saving it in a data base.
b. adaptive Image segmentation for the input image.
c. Texture Features extraction using GLCM Matrix in different Direction.
d. Test unknown image by calculate the texture features which compared with data base.