Link Graph Analysis for Adult Images Classification

TL;DR

This paper presents a graph-based algorithm that leverages website-image link structures and score propagation to improve the classification of adult images, significantly increasing recall over simpler methods.

Contribution

It introduces an iterative score propagation method on bipartite website-image graphs for adult image classification, enhancing recall compared to basic link-based approaches.

Findings

Increased classification recall by 17% over simple link-based methods

Utilized website scores from text classifiers as initial vertex scores

Effective on Internet-scale data for adult image filtering

Abstract

In order to protect an image search engine's users from undesirable results adult images' classifier should be built. The information about links from websites to images is employed to create such a classifier. These links are represented as a bipartite website-image graph. Each vertex is equipped with scores of adultness and decentness. The scores for image vertexes are initialized with zero, those for website vertexes are initialized according to a text-based website classifier. An iterative algorithm that propagates scores within a website-image graph is described. The scores obtained are used to classify images by choosing an appropriate threshold. The experiments on Internet-scale data have shown that the algorithm under consideration increases classification recall by 17% in comparison with a simple algorithm which classifies an image as adult if it is connected with at least one adult site (at the same precision level).