Paper Info
Title
Reduced Complexity Image Clustering Based On Camera Fingerprints
Abstract
This work presents a reduced complexity image clustering (RCIC) algorithm that blindly groups images based on their camera fingerprint. The algorithm does not need any prior information and can be implemented without and with attraction, to refine clusters. After a camera fingerprint is estimated for each image in the data set, a fingerprint is randomly selected as reference fingerprint and a cluster is constructed using this fingerprint as centroid. The clustered fingerprints are removed from the data set and the remaining fingerprints are clustered repeating the same process. A further attraction stage can be included, in which a similar algorithm is performed using the centroids of the clusters found after the first stage. Despite its simplicity, results show that RCIC algorithm has lower computational cost than existing algorithms, while maintaining similar or even better performance. Moreover, the performance of the proposed algorithm is not affected significantly when the number of cameras in the data set is much larger than the average number of images from each camera.
Year
DOI
Venue
2019
10.1109/icassp.2019.8683754
2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)
Keywords
Field
DocType
Camera fingerprints, large scale clustering, complexity reduction, image forensics
Cluster (physics),Pattern recognition,Computer science,Reduction (complexity),Fingerprint,Image forensics,Artificial intelligence,Cluster analysis,Centroid
Conference
ISSN
Citations 
PageRank 
1520-6149
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Sahib Khan102.03
Tiziano Bianchi2100362.55