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In recent years, dictionary learning (DL) has shown significant potential in various classification tasks. However, most of previous works aim to learn a synthesis dictionary. The other major category of DL-analysis dictionary learning has not been fully exploited yet. This paper proposes a novel DL method, named Topology Preserving Dictionary Learning (TPDL). First, we propose a triplet-constraint-based...
The use of top-down categorization information in bottom-up semantic segmentation can significantly improve its performance. The basic Conditional Random Field (CR-F) model can capture the local contexture information, while the locality-consistent sparse representation can obtain the category-level priors and the relationship infeature space. In this paper, we propose a novel semantic segmentation...
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