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

In this paper we present a methodology and the corresponding Python library ¹ 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.