About: Beyond Novelty Detection: Incongruent Events, when General and Specific Classifiers Disagree     Goto   Sponge   Distinct   Permalink

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Description
  • 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.
  • 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. (en)
Title
  • Beyond Novelty Detection: Incongruent Events, when General and Specific Classifiers Disagree
  • Beyond Novelty Detection: Incongruent Events, when General and Specific Classifiers Disagree (en)
skos:prefLabel
  • Beyond Novelty Detection: Incongruent Events, when General and Specific Classifiers Disagree
  • Beyond Novelty Detection: Incongruent Events, when General and Specific Classifiers Disagree (en)
skos:notation
  • RIV/68407700:21230/12:00196128!RIV13-MSM-21230___
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  • 124795
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  • RIV/68407700:21230/12:00196128
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  • Novelty Detection; Categorization; Object Recognition; Out Of Vocabulary Words (en)
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  • US - Spojené státy americké
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  • [33597FB4021D]
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  • IEEE Transactions on Pattern Analysis and Machine Intelligence
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  • 34
http://linked.open...iv/tvurceVysledku
  • Pajdla, Tomáš
  • Havlena, Michal
  • Anemüller, J.
  • Bach, J.
  • Hermansky, H.
  • Kombrink, S.
  • Nater, F.
  • Ohl, F. W.
  • Pavel, M.
  • Van Gool, L.
  • Weinshall, D.
  • Zweig, A.
http://linked.open...ain/vavai/riv/wos
  • 000307522700002
issn
  • 0162-8828
number of pages
http://bibframe.org/vocab/doi
  • 10.1109/TPAMI.2011.279
http://localhost/t...ganizacniJednotka
  • 21230
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