Content-Based Filtering for Video Sharing Social Networks

TL;DR

This paper presents a simple, effective content filtering method for video social networks that combines diverse features and uses codebooks of spatiotemporal descriptors to detect unwanted content like pornography and violence.

Contribution

It introduces a novel, straightforward approach that integrates multiple evidence sources and emphasizes spatiotemporal features, outperforming static feature-based methods.

Findings

Effective detection of unwanted videos in social networks.

Codebook of spatiotemporal descriptors is crucial for success.

Method performs well across diverse challenging datasets.

Abstract

In this paper we compare the use of several features in the task of content filtering for video social networks, a very challenging task, not only because the unwanted content is related to very high-level semantic concepts (e.g., pornography, violence, etc.) but also because videos from social networks are extremely assorted, preventing the use of constrained a priori information. We propose a simple method, able to combine diverse evidence, coming from different features and various video elements (entire video, shots, frames, keyframes, etc.). We evaluate our method in three social network applications, related to the detection of unwanted content - pornographic videos, violent videos, and videos posted to artificially manipulate popularity scores. Using challenging test databases, we show that this simple scheme is able to obtain good results, provided that adequate features are chosen. Moreover, we establish a representation using codebooks of spatiotemporal local descriptors as critical to the success of the method in all three contexts. This is consequential, since the state-of-the-art still relies heavily on static features for the tasks addressed.