LBP Feature Extraction and Statistical Pooling-Based Image Spam Detection Model

Authors : Aytaç KAŞOĞLU, Orhan YAMAN

Pages : 24-35

Doi:10.53070/bbd.1268221

View : 47 | Download : 76

Publication Date : 2023-06-08

Article Type : Research Paper

Abstract :Email, which stands for electronic mail, is a form of digital communication between two or more individuals. These technological instruments that facilitate communication can have a positive and negative impact on our lives due to junk e-mails, widely known as spam mail. These spam messages, which are typically delivered for commercial purposes by organizations/individuals for indirect or direct benefits, not only distract people but also consume a significant amount of system resources such as processing power, memory, and network bandwidth. In this study, a method based on LBP insert ignore into journalissuearticles values(Local Binary Patterns); feature extraction and statistical pooling is proposed to classify spam or raw insert ignore into journalissuearticles values(non-spam); images. Two datasets are used to test the proposed method. The ISH dataset is widely used in the literature and contains 1738 images. In addition to this dataset, the dataset our collect consists of 1015 images in total. Feature extraction was performed on these images. Obtained features were classified by SVM insert ignore into journalissuearticles values(Support Vector Machine); algorithm. In the proposed method, 98.56% and 79.01% accuracy were calculated for the ISH dataset and our collected dataset, respectively. The results obtained were compared with the studies in the literature.
Keywords : Spam görüntü algılama, Makine öğrenimi, LBP özellik çıkarma, SVM