# A Web Page Classifier Library Based on Random Image Content Analysis Using Deep Learning

Source: [A Web Page Classifier Library Based on Random Image Content Analysis Using Deep Learning](<https://www.sota2.com/research/paper/68fa66d31e209131cea12a9a>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:00:21.133Z

Authors: Leonardo Espinosa Leal, Kaj-Mikael Bj\\"ork, Amaury Lendasse, Anton Akusok

Published: 2019-12-18T14:50:18.000Z

## Abstract

In this paper, we present a methodology and the corresponding Python library 1 for the classification of webpages. Our 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 build upon the features extracted from images by a pre-trained deep 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 into the studies classes 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. Further research explores the possibilities for the developed methodology to also apply in image classification for healthcare applications.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1912.08644>)

[PDF](<https://arxiv.org/pdf/1912.08644.pdf>)