A theory is proposed that formulates the conditions under which P-N learning guarantees improvement of the initial classifier and validate it on synthetic and real data. P-N learning is applied to the problem of on-line learning of object detector during tracking.
We show that the performance of a binary clas- sifier can be significantly improved by the processing of structured unlabeled data, i.e. data are structured if know- ing the label of one example restricts the labeling of the others. An accurate object detector can be learned from a single example and an unlabeled video sequence where the object may occur. The algorithm is compared with related approaches and state-of-the-art is achieved on a variety of objects (faces, pedestrians, cars, motorbikes and animals).