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

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

Forskningsoutput: Kapitel i bok/rapport/konferenshandlingKonferensbidragVetenskapligPeer review

5 Citeringar (Scopus)


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.

Titel på värdpublikationProceedings of the 11th PErvasive Technologies Related to Assistive Environments Conference, PETRA 2018
Antal sidor4
FörlagACM - Association for Computing Machinery
ISBN (elektroniskt)9781450363907
StatusPublicerad - 26.06.2018
MoE-publikationstypA4 Artikel i en konferenspublikation
Evenemang11th ACM International Conference on PErvasive Technologies Related to Assistive Environments, PETRA 2018 - Corfu, Grekland
Varaktighet: 26.06.201829.06.2018


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