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In this paper, we propose an unsupervised cluster method via a multi-task learning strategy, called Mt-Cluster. Our MtCluster learns a cluster-specific dictionary for each cluster to represent its sample signals and a shared common pattern pool (the commonality) for the essentially complemental representation. By treating learning the cluster-specific dictionary as a single task, MtCluster works in...
Studies on human faculties of scene recognition have lead to two broad classifications of the perceived information: local and global. It has been shown that both are processed separately and combined towards final category assignment. Recently, it was suggested that accuracy of computational models for local information closely match human performance, while it is not so for current global representations...
This paper introduces a novel video presentation term spatial-temporal pyramid sparse coding (STPSC) which characterizes both the spatial and temporal aspects of the video. Specifically, the co-occurrences of visual words are computed with respect to the spatial layout and the sequencing of the features in the video. The representation captures both the spatial arrangement and the temporal relationship...
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