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- W2244482471 abstract "We present a variant of algorithms to detect human in still images exposing covariance matrices as the representation of the feature. By defining the distance between data features, the workspace is transformed into a metric space. On this metric space, we implemented k-medoids as a clustering algorithm to specify appropriate label assignment for each data feature and presenting the center point of each cluster. The newly coming data feature is classified based on its distance to particular cluster. This approach is considered simple and easy to implement. By using a simple classification procedure, we could capture the degree of discrimination - how well it represent the object and discriminate others - of this feature representation. The algorithm is tested on INRIA human database where the detection rates are observed at least same as the previous approaches." @default.
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- W2244482471 date "2008-10-01" @default.
- W2244482471 modified "2023-09-26" @default.
- W2244482471 title "Human Detection via Unsupervised Learning on Metric Space" @default.
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