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  1. Proceedings of the First ACM workshop on Multimedia in forensics (MiFor '09)
  2. Graffiti-ID: matching and retrieval of graffiti images
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Graffiti-ID: matching and retrieval of graffiti images
Videntifierâ„¢ forensic: a new law enforcement service for automatic identification of illegal video material
Digital forgery estimation into DCT domain: a critical analysis
Single view geometry and active camera networks made easy
Temporal normalization of videos using visual speech
Image spam clustering: an unsupervised approach
A new approach for JPEG resize and image splicing detection
Multi-target tracking in time-lapse video forensics
Video surveillance and multimedia forensics: an application to trajectory analysis
Design and deployment of a digital forensics service platform for online videos
Exposing digital video forgery by ghost shadow artifact

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Graffiti-ID: matching and retrieval of graffiti images

Content Provider ACM Digital Library
Author Jin, Rong Jain, Anil K. Lee, Jung-Eun
Abstract Graffiti are abundant in most urban neighborhoods and are considered a nuisance and an eyesore. Yet, law enforcement agencies have found them to be useful for understanding gang activities, and uncovering the extent of a gang's territory in large metropolitan areas. The current method for matching and retrieving graffiti is based on a manual database search that is not only inaccurate but also time consuming. We present a content-based image retrieval (CBIR) system for automatic matching and retrieval of graffiti images. Our system represents each graffiti image by a bag of SIFT (Scale Invariant Feature Transform) features. The similarity between a query image and a graffiti image in the database is computed based on the number of matched SIFT features between the two images under certain geometric constraints. Experimental results on two graffiti databases with thousands of graffiti images show encouraging results.
Starting Page 1
Ending Page 6
Page Count 6
File Format PDF
ISBN 9781605587554
DOI 10.1145/1631081.1631083
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2009-10-23
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Graffiti Gangs Tagging Content-based image retrieval
Content Type Text
Resource Type Article
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