Learning invariance from transformation sequences
Neural Computation
Self-Organizing Maps
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Querying Spatio-temporal Patterns in Mobile Phone-Call Databases
MDM '10 Proceedings of the 2010 Eleventh International Conference on Mobile Data Management
Canonical correlation analysis using within-class coupling
Pattern Recognition Letters
Matching images of never-before-seen individuals in large law enforcement databases
ISI'06 Proceedings of the 4th IEEE international conference on Intelligence and Security Informatics
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Location and time are two critical aspects of most security-related events, and thus, spatiotemporal data analysis plays a central role in many security-related applications. The human brain has great capabilities of developing invariant representations of objects by taking advantage of both spatial similarity of features of objects/events and their relative timings (temporal information). Trace learning rule is one well-known solution for this problem of combining temporal relations with spatial proximity in clustering tasks such as the one performed by self organizing maps. In this work, we investigate a two stage mechanism: i) finding local clusters using spatial proximity, ii) grouping these clusters as suggested by temporal continuity patterns. We show our experimental results on a movie created from face images.