Mobile phone spam image detection based on graph partitioning with Pyramid Histogram of Visual Words image descriptor

Image spams have been annoying users everywhere and it has also been increasingly appearing in mobile phones these days. In accordance with more sophisticated spam filtering system, spams are being more intelligent and have caused severe social problems. However, there has not been effective solution for detecting mobile phone spam images yet. Due to the insufficient spam image data in mobile phones, training the predictive model is quite hard. To resolve this issue, we recently proposed a phone spam image filtering system using e-mail spam images and showed that using e-mail spam data is fairly meaningful in improving the performance of phone spam image detection. In this paper, we further investigate the effectiveness of utilizing the graph structure in e-mail spam data. Furthermore, the classification performance behavior depending on different image descriptors of Pyramid Histogram of Visual Words (PHOW) and RGB histogram is explored extensively.