Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
AnnoSearch: Image Auto-Annotation by Search
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Graffiti-ID: matching and retrieval of graffiti images
MiFor '09 Proceedings of the First ACM workshop on Multimedia in forensics
Third ACM international workshop on multimedia in forensics and intelligence (MiFor 2011)
MM '11 Proceedings of the 19th ACM international conference on Multimedia
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Identifying criminal gangs and monikers is one of the most important tasks for graffiti analysis in low enforcement. In current practice, this is typically performed manually by the law enforcement officers, which is not only time-consuming but also results in limited identification performance. In this paper, we present a system that is able to automatically identify the gang or the moniker for a given graffiti image. The key idea of our system is as follows: given a graffiti query, first find a candidate list of the most similar images from a large graffiti database based on visual and content similarity, and then return the most frequent gang/moniker names associated with the candidate list as the tag for the query graffiti. Our experiments with a large database of graffiti images collected by the Orange County Sheriff's Department in California show that our system is (i) effective in determining the gang/moniker of graffiti, and (ii) scalable to large image databases of graffiti.