Welcome to the InfoSci Platform
Image Spam: Feature Extraction

Image Spam: Feature Extraction

Copyright: © 2017 |Pages: 32
ISBN13: 9781683180135|ISBN10: 1683180135|EISBN13: 9781683180142
DOI: 10.4018/978-1-68318-013-5.ch003
Cite Chapter Cite Chapter

MLA

Sunita Vikrant Dhavale. "Image Spam: Feature Extraction." Advanced Image-Based Spam Detection and Filtering Techniques, IGI Global, 2017, pp.58-89. https://doi.org/10.4018/978-1-68318-013-5.ch003

APA

S. Dhavale (2017). Image Spam: Feature Extraction. IGI Global. https://doi.org/10.4018/978-1-68318-013-5.ch003

Chicago

Sunita Vikrant Dhavale. "Image Spam: Feature Extraction." In Advanced Image-Based Spam Detection and Filtering Techniques. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-68318-013-5.ch003

Export Reference

Mendeley
Favorite

Abstract

Spam features represent the unique and special characteristics associated with spam, which are further used to differentiate them from other genuine messages. Each message m is processed by a feature extraction module to represent m in terms of n dimensional feature vector x = (x1, x2, …, xn) containing n features. This feature vector consists of many such features extracted from spam. In case of text based spam filters, a feature can be a word and a feature vector may be composed of various words extracted from spam. Each spam is associated with one feature vector. Based on the characteristics discussed in previous chapter, we will try to extract different features capturing those unique characteristics from image spam, in order to build the robust spam detection algorithms further. These features are broadly classified into high level metadata features, low level image features like color features, grayscale features, texture related features and embedded text related features.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.