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A common design of an object recognition system has two steps, a detection step followed by a foreground within class classification step. For example, consider face detection by a boosted cascade of detectors followed by face ID recognition via One-Vs-All (OVA) classifiers. Another example is human detection followed by pose recognition. Although the detection step can be quite fast, the foreground within-class classification process can be slow and becomes a bottleneck. In this paper, the authors formulate a filter-and-refine scheme, where the binary outputs of the weak classifiers in a boosted detector are used to identify a small number of candidate foreground state hypotheses quickly via Hamming distance or weighted Hamming distance.
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