Description

The identification of Spam images were more helpful in the networking since they consume a lot of bandwidth. The input images were classified as Spam images or Non Spam images by analyzing the texture of the images. The LBP patterns and Gabour features were more reliable pattern recognition tools. An image texture is a set of metrics calculated in image processing designed to quantify the perceived texture of an image. Image texture gives us information about the spatial arrangement of color or intensities in an image or selected region of an image. Image textures can be artificially created or found in natural scenes captured in an image. Image textures are one way that can be used to help in segmentation or classification of images. To analyze an image texture in computer graphics, there are two ways to approach the issue: Structured Approach and Statistical Approach. The features were extracted based on the texture patterns extracted. The extracted values were saved. The system is initially trained with dataset images that contains spam and non-spam images. The trained features and the test image features were given as input to the classifiers. The classifier classifies whether the input image is spam image or not. The performance of the process is measured by measuring the accuracy of the process. The accuracy denotes the rate at which the classifier classifies the result.