Content-Based Filtering for Video Sharing Social Networks
Eduardo Valle, Sandra de Avila, Antonio da Luz Jr., Fillipe de Souza,, Marcelo Coelho, Arnaldo Ara\'ujo

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…
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Taxonomy
TopicsAdvanced Image and Video Retrieval Techniques · Video Analysis and Summarization · Image Retrieval and Classification Techniques
