Feature Learning for Conditional Random Fields and its Application to Gesture Recognition
msra(2013)
摘要
Conditional random fields (CRFs) have been successful in many sequence label- ing tasks, which conventionally rely on a hand-craft feature representation of input data. However, a powerful data representation could be another determining fac- tor of the performance, which has not attracted enough attention yet. We describe a novel sequence labeling framework for gesture recognition, which builds a su- pervised CRF and an unsupervised dynamic model on a shared nonlinear feature transformation neural network. The model is a case of transfer learning that jointly optimizes two learning tasks together with learning a meaningful feature repre- sentation of input data. We demonstrate a gesture recognition system that yields a significant improvement of recognition accuracy over conventional CRFs.
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