EFFICIENT RETRIEVAL OF ANOMALOUS EVENTS WITH PRIORITY LEARNING

Local models learned from anomaly detection are used to rank detected anomalies. The local models include image feature values extracted from an image field of video image data with respect to different predefined spatial and temporal local units, wherein anomaly results are determined by failures t...

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Bibliographic Details
Main Authors DATTA ANKUR, PALURI BALAMANOHAR, ZHAI YUN, PANKANTI SHARATHCHANDRA U
Format Patent
LanguageEnglish
Published 22.11.2012
Subjects
CALCULATING
COMPUTING
COUNTING
PHYSICS
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Summary:Local models learned from anomaly detection are used to rank detected anomalies. The local models include image feature values extracted from an image field of video image data with respect to different predefined spatial and temporal local units, wherein anomaly results are determined by failures to fit to applied anomaly detection module local models. Image features values extracted from the image field local units associated with anomaly results are normalized, and image feature values extracted from the image field local units are clustered. Weights for anomaly results are learned as a function of the relations of the normalized extracted image feature values to the clustered image feature values. The normalized values are multiplied by the learned weights to generate ranking values to rank the anomalies.
Bibliography:Application Number: US201113110331