IS = { zkontrolovano 31 Jan 2011 },
  UPDATE  = { 2010-07-27 },
  author = {Kalal, Zdenek and Matas, Ji{\v r}{\' i} and 
            Mikolajczyk, Krystian},
  title = {P-N Learning: Bootstrapping Binary Classifiers by
           Structural Constraints},
  booktitle = {{CVPR} 2010: Proceedings of the 2010 IEEE Computer
           Society Conference on Computer Vision and Pattern Recognition},
  language = {english},
  year = {2010},
  pages = {49--56},
  month = {June},
  annote = { This paper shows 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. We
    propose a novel paradigm for training a binary classifier from
    labeled and unlabeled examples that we call P-N learning. The
    learning process is guided by positive (P) and negative (N)
    constraints which restrict the label- ing of the unlabeled
    set. P-N learning evaluates the clas- sifier on the unlabeled
    data, identifies examples that have been classified in
    contradiction with structural constraints and augments the
    training set with the corrected samples in an iterative process.
    We propose a theory that formu- lates the conditions under which
    P-N learning guarantees improvement of the initial classifier and
    validate it on syn- thetic and real data. P-N learning is applied
    to the problem of on-line learning of object detector during
    tracking.  We show that 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). },
  keywords =    {tracking, p-n learning, long-term, TLD},
  publisher =   {Omnipress},
  address =     {Madison, USA},
  book_pages =  {3523},
  prestige =    {important},
  day = {13--18},
  isbn = {978-1-4244-6984-0},
  issn = {1063-6919},
  venue = {San Francisco, USA},
  project = {GACR 102/07/1317},
  www = {http://cmp.felk.cvut.cz/~matas/papers/kalal-pn_learning-cvpr10.pdf},