Abstract:
The explosion of Image spam emails has
prompted the development of numerous spam
filtering techniques. This paper proposes an
efficient image spam filtering system using three
methods. The first method, File properties,
analyses high level features in order to reduce
computation cost. The second approach uses
Hue, Saturation, Intensity (HSI) color model of
histogram and the third method uses Hough line
Detection. These three methods filter the image
spam by analyzing both images including text
and image. The images are collected from three
different datasets that are Priceton, Image Spam
Hunter and Spam Archieve Datasets. Support
Vector Machine (SVM) classifies the input image
is spam image or normal image. The
experimental result shows the accuracy of
different methods on different datasets and
evaluates computation time. Among the three
methods, Hough line can detect the input image
within the minimum processing time required.
