| 摘要 |
<P>PROBLEM TO BE SOLVED: To provide a training method for adapting a generic classifier to a particular scene, which has been unknown or unavailable when the classifier was trained. <P>SOLUTION: A generic classifier is adapted to detect an object in a particular scene. The particular scene was unknown when the classifier was trained with generic training data. A camera acquires a video of frames of the particular scene. A model of the particular scene model is constructed by using the frames in the video. The classifier is applied to the model to select negative examples, and new negative examples are added to the training data while removing another set of existing negative examples from the training data based on an uncertainty measure. Selected positive examples are also added to the training data and the classifier is retrained until a desired accuracy level is reached to obtain a scene specific classifier. <P>COPYRIGHT: (C)2012,JPO&INPIT |
