Computer Science – Computer Vision and Pattern Recognition
Scientific paper
2011-01-12
Computer Science
Computer Vision and Pattern Recognition
Scientific paper
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.
Araújo Arnaldo
Avila Sandra de
Coelho Marcelo
Luz Antonio da Jr.
Souza Fillipe de
No associations
LandOfFree
Content-Based Filtering for Video Sharing Social Networks does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Content-Based Filtering for Video Sharing Social Networks, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Content-Based Filtering for Video Sharing Social Networks will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-265573