Beyond Novelty Detection: Incongruent Events, when General and Specific Classifiers Disagree

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F12%3A00196128" target="_blank" >RIV/68407700:21230/12:00196128 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/TPAMI.2011.279" target="_blank" >http://dx.doi.org/10.1109/TPAMI.2011.279</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TPAMI.2011.279" target="_blank" >10.1109/TPAMI.2011.279</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Beyond Novelty Detection: Incongruent Events, when General and Specific Classifiers Disagree

Popis výsledku v původním jazyce

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

Název v anglickém jazyce

Beyond Novelty Detection: Incongruent Events, when General and Specific Classifiers Disagree

Popis výsledku anglicky

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

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

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Projekt

—

Návaznosti

R - Projekt Ramcoveho programu EK

Rok uplatnění

2012

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE Transactions on Pattern Analysis and Machine Intelligence

ISSN

0162-8828

e-ISSN

—

Svazek periodika

34

Číslo periodika v rámci svazku

10

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

16

Strana od-do

1886-1901

Kód UT WoS článku

000307522700002

EID výsledku v databázi Scopus

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