@Article{Weinshall-et-al-PAMI-2012,
  IS = { zkontrolovano 10 Oct 2012 },
  UPDATE  = { 2012-03-07 },
  author =	{Weinshall, Daphna and Zweig, Alon and Hermansky, Hynek and
		 Kombrink, Stefan and Ohl, Frank W. and
		 Anem{\" u}ller, J{\" o}rn and Bach, J{\" o}rg-Hendrik and
		 Van Gool, Luc and Nater, Fabian and Pajdla, Tomas and
		 Havlena, Michal and Pavel, Misha},
  title =	{Beyond Novelty Detection: Incongruent Events, when General and
		 Specific Classifiers Disagree},
  year =	{2012},
  pages =       {1886-1901},
  journal =	{{IEEE} Transactions on Pattern Analysis and Machine Intelligence},
  publisher =	{IEEE Computer Society},
  address =	{Los Alamitos, USA},
  issn =	{0162-8828},
  volume =      {34},
  number =      {10},
  month =       {October},
  annote   = { Unexpected stimuli are a challenge to any machine
    learning algorithm. Here we identify distinct types of unexpected
    events, when general level and specific level classifiers give
    conflicting predictions. We define a formal framework for the
    representation and processing of incongruent events: Starting from
    the notion of label hierarchy, we show how partial order on labels
    can be deduced from such hierarchies. For each event, we compute
    its probability in different ways, based on adjacent levels in the
    label hierarchy. An incongruent event is an event where the
    probability computed based on some more specific level is much
    smaller than the probability computed based on some more general
    level, leading to conflicting predictions.  Algorithms are derived
    to detect incongruent events from different types of hierarchies,
    different applications and a variety of data types. We present
    promising results for the detection of novel visual and audio
    objects, and new patterns of motion in video. We also discuss the
    detection of Out Of Vocabulary words in speech recognition, and
    the detection of incongruent events in a multimodal audiovisual
    scenario. },
  keywords =	{Novelty Detection, Categorization, Object Recognition, 
                 Out Of Vocabulary Words},
  authorship =	{9-9-9-9-8-8-8-8-8-8-8-8},
  project =	{FP6-IST-027787 DIRAC},
  psurl = { [10.1109/TPAMI.2011.279.pdf] },
  doi =         {10.1109/TPAMI.2011.279},
  ut_isi =      {000307522700002},
}