A Method for Image Spam Detection Using Texture Features

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Monireh sadat Hosseini and Mohammad Rahmati


By increasing of e-mail, the received junk mail has become a challenge, which is called spam e-mails. To detect image spam, computer vision techniques can be used. In this article, a method to increase of the accuracy of identification and classification of spam or non-spam valid images is personated. In this method, image texture features are used to evaluate the image. In this study, the gray level co-occurrence matrix (GLCM) is used that is one of the characteristics of the texture. After extraction matrixes from images , for each image, was obtained 22 features. Then the k-nearest neighbor classifier (KNN) and naive Bayesian (NB) are used to classify images with features that obtained of each images. The images obtained from the both of works database Dredze and ISH. In this method, presented results were given with compare the last works indicative of importance classification in accuracy

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