Abstract: This paper describes a hybrid method in the object classification for computer digital images. method in this paper has been designed and developed to recognize a typical texture features for certain object. The basic approach used here is that the textures features values that extracted from gray level co-occurrence matrices (GLCM) can show the typical values for features analysis in classification. An artificial neural networks using error multilayer back propagation network has been used for texture analysis and object classification. The obtained results of different types of images areas like "seas" "non-seas" and "background" as unknown images was characterized in a good range.

This paper introduces a new approach of object classification for certain type which is a part of image processing using Gray level cooccurrence matrix (GLCM) as an example the class of seas images has been used for classification and the approach can be applied for single class image in different patterns. The simplest way practice in this paper is a classification of any class images into patterns using adaptive segmentation with the use of their textures features in different direction of GLCM matrix to train the artificial neural networks (back propagation neural network used here). This association between local trained features values and recognized class sea as an example led to obtain a good results using this method .
Another direction in this paper is extracting the texture feature for unknown image and let the neural detect the type of this image using a neural network and the approach applied for variety images. Patterns features 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) .