Abstract
In This Paper We Present a Methodology and the Corresponding Python Library1 for the Classification of Webpages. the Method Retrieves a Fixed Number of Images from a Given Webpage, and based on Them Classifies the Webpage into a Set of Established Classes with a Given Probability. the Library Trains a Random Forest Model Built Upon the Features Extracted from Images by a Pre-Trained Neural Network. the Implementation is Tested by Recognizing Weapon Class Webpages in a Curated List of 3859 Websites. the Results Show that the Best Method of Classifying a Webpage among the Classes of Interest is to Assign the Class According to the Maximum Probability of Any Image Belonging to This (Weapon) Class Being above the Threshold, Across All the Retrieved Images. Our Finding Can Have an Important Impact in the Treatment of Internet Addictions.
Recommended Citation
L. E. Leal et al., "A Web Page Classifier Library based on Random Image Content Analysis using Deep Learning," ACM International Conference Proceeding Series, pp. 13 - 16, Association for Computing Machinery, Jun 2018.
The definitive version is available at https://doi.org/10.1145/3197768.3201525
Department(s)
Engineering Management and Systems Engineering
Keywords and Phrases
Computer vision; Deep learning; Webpage classification
International Standard Book Number (ISBN)
978-145036390-7
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Association for Computing Machinery, All rights reserved.
Publication Date
26 Jun 2018
Comments
Clorox Company, Grant None