Statistical Strategy for Object Class Recognition Using Part Detectors
نویسندگان
چکیده
This paper presents a method for the recognition of object class once parts have been detected. The recognition task is formulated as a graph search problem in which the optimal score function is derived from Bayesian maximum a posteriori estimation. An efficient graph search algorithm is used to find the optimal solution. The method differs from previous approaches, such as one proposed by Burl and Perona [3], in that the optimal score function is derived in a generative manner. As a result, the score function suggests a powerful connection to the problems of part selection, detection and threshold setting for part detectors.
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تاریخ انتشار 2001