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Spatio-temporal HAC

Exemplar Extraction using Spatio-Temporal Hierarchical Agglomerative Clustering for Face Recognition in Video. Spatio-temporal HAC Proposed: Effective selection of exemplars by incorporating spatio-temporal information for clustering of training data

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Spatio-temporal HAC

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  1. Exemplar Extraction using Spatio-Temporal HierarchicalAgglomerative Clustering for Face Recognition in Video • Spatio-temporal HAC • Proposed: Effective selection of exemplars by incorporating spatio-temporal information for clustering of training data • No temporal consideration in most video-based FR using training exemplars • 2 variants: Global fusion & Local perturbation • Global method  Blends contribution of spatial and temporal distances • Local method  Applies perturbation of spatial and temporal distances based on local neighborhood relationships • Performance & Analysis • Outperform conventional exemplar selection methods (k-means, HAC) on tested features • Promising results underline importance of both spatial and temporal relationships between image frames in video John See and Chikkannan Eswaran Faculty of Information Technology, Multimedia University, Malaysia

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