The Quadtree and Related Hierarchical Data Structures
ACM Computing Surveys (CSUR)
An Extendible Hash for Multi-Precision Similarity Querying of Image Databases
Proceedings of the 27th International Conference on Very Large Data Bases
Region Queries without Segmentation for Image Retrieval by Content
VISUAL '99 Proceedings of the Third International Conference on Visual Information and Information Systems
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
A generalized metric distance between hierarchically partitioned images
MDM '05 Proceedings of the 6th international workshop on Multimedia data mining: mining integrated media and complex data
Tracking people across disjoint camera views by an illumination-tolerant appearance representation
Machine Vision and Applications
Viewpoint Invariant Pedestrian Recognition with an Ensemble of Localized Features
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Region covariance: a fast descriptor for detection and classification
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Learning multi-scale block local binary patterns for face recognition
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
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In many surveillance systems, there is a need to determine if a given object (person, group of persons, vehicle, ...) has already been observed over a network of cameras. It is the object re-identification problem. Solving this problem involves matching observation of objects across disjoint camera views. Uncalibrated fixed or mobile cameras with non-overlapping field of view generate uncontrolled variation in view point, background and lighting. In such situations, a robust and invariant image description is required. A multi-scale covariance image descriptor and a quadtree based scheme are proposed to describe any object of interest. We describe a fast method for computation of multi-scale covariance descriptor. The descriptor is evaluated in person re-identification application using the VIPeR dataset. We show that the proposed multi-scale approach outperforms existing mono-scale image description methods.