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

TL;DR

This paper introduces a Python library that classifies webpages by analyzing images extracted from them using deep learning features and a random forest model, demonstrating effectiveness in weapon webpage detection.

Contribution

The paper presents a novel webpage classification method leveraging deep image features and a random forest, with an open-source Python library for practical use.

Findings

Effective weapon webpage recognition with threshold-based image classification.

Deep features from pre-trained networks improve webpage classification accuracy.

Potential application in healthcare image classification explored.

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.