A web page classifier library based on random image content analysis using deep learning

Leonardo Espinosa Leal, Kaj Mikael Björk, Amaury Lendasse, Anton Akusok

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings of the 11th PErvasive Technologies Related to Assistive Environments Conference, PETRA 2018
Number of pages4
PublisherACM - Association for Computing Machinery
Publication date26.06.2018
Pages13-16
ISBN (Electronic)9781450363907
DOIs
Publication statusPublished - 26.06.2018
MoE publication typeA4 Article in conference proceedings
Event11th ACM International Conference on PErvasive Technologies Related to Assistive Environments, PETRA 2018 - Corfu, Greece
Duration: 26.06.201829.06.2018

Keywords

  • Computer vision
  • Deep learning
  • Webpage classification
  • 512 Business and Management

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  • Cite this

    Leal, L. E., Björk, K. M., Lendasse, A., & Akusok, A. (2018). A web page classifier library based on random image content analysis using deep learning. In Proceedings of the 11th PErvasive Technologies Related to Assistive Environments Conference, PETRA 2018 (pp. 13-16). ACM - Association for Computing Machinery. https://doi.org/10.1145/3197768.3201525