Graffiti-ID: matching and retrieval of graffiti images

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.

[1]  F. Bookstein.  Thin-plate splines and decomposition of deformation , 1989 .

[2]  Rong Jin,et al.  An efficient key point quantization algorithm for large scale image retrieval , 2009, LS-MMRM '09.

[3]  A.K. Jain,et al.  Scars, marks and tattoos (SMT): Soft biometric for suspect and victim identification , 2008, 2008 Biometrics Symposium.

[4]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[5]  Yan Ke,et al.  PCA-SIFT: a more distinctive representation for local image descriptors , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[6]  Michael D. Garris,et al.  NIST Special Database 27 Fingerprint Minutiae From Latent and Matching Tenprint Images , 2000 .

[7]  Cordelia Schmid,et al.  A performance evaluation of local descriptors , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Elizabeth D. Mynatt.  The Writing on the Wall , 1999, INTERACT.

[9]  H Moon,et al.  Computational and Performance Aspects of PCA-Based Face-Recognition Algorithms , 2001, Perception.

[10]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[11]  Fred L. Bookstein,et al.  Principal Warps: Thin-Plate Splines and the Decomposition of Deformations , 1989, IEEE Trans. Pattern Anal. Mach. Intell..