EFFICIENT RETRIEVAL OF ANOMALOUS EVENTS WITH PRIORITY LEARNING
First Claim
1. A method for using models learned from anomaly detection to rank detected anomalies, the method comprising:
- retrieving a plurality of anomaly results from an anomaly detection module comprising a plurality of local models in response to an input query for an anomaly, wherein the anomaly detection module local models comprise image feature values extracted from an image field of video image data with respect to each of a plurality of different predefined spatial and temporal local units, and each of the plurality of anomaly results are determined by respective failures to fit to applied ones of the anomaly detection module local models;
normalizing each of a plurality of values of image features extracted from the image field local units and that are associated with each of the plurality of anomaly results;
clustering image feature values extracted from the each image field local units that are associated with the each of the plurality of anomaly results;
learning each of a plurality of weights for each of the anomaly results as a function of a relation of their normalized values of the extracted image features to the clustered image feature values extracted from the each respective associated image field local units;
multiplying the normalized values of extracted features of each of the plurality of anomalies by their respective learned weights to generate respective ranking values; and
ranking the plurality of anomalies by their generated respective ranking values.
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Abstract
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.
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Citations
22 Claims
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1. A method for using models learned from anomaly detection to rank detected anomalies, the method comprising:
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retrieving a plurality of anomaly results from an anomaly detection module comprising a plurality of local models in response to an input query for an anomaly, wherein the anomaly detection module local models comprise image feature values extracted from an image field of video image data with respect to each of a plurality of different predefined spatial and temporal local units, and each of the plurality of anomaly results are determined by respective failures to fit to applied ones of the anomaly detection module local models; normalizing each of a plurality of values of image features extracted from the image field local units and that are associated with each of the plurality of anomaly results; clustering image feature values extracted from the each image field local units that are associated with the each of the plurality of anomaly results; learning each of a plurality of weights for each of the anomaly results as a function of a relation of their normalized values of the extracted image features to the clustered image feature values extracted from the each respective associated image field local units; multiplying the normalized values of extracted features of each of the plurality of anomalies by their respective learned weights to generate respective ranking values; and ranking the plurality of anomalies by their generated respective ranking values. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A system, comprising:
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a processing unit, computer readable memory and a computer readable storage medium; first program instructions to retrieve a plurality of anomaly results from an anomaly detection module comprising a plurality of local models in response to an input query for an anomaly, wherein the anomaly detection module local models comprise image feature values extracted from an image field of video image data with respect to each of a plurality of different predefined spatial and temporal local units, and each of the plurality of anomaly results are determined by respective failures to fit to applied ones of the anomaly detection module local models; second program instructions to normalize each of a plurality of values of image features extracted from the image field local units and that are associated with each of the plurality of anomaly results; third program instructions to cluster image feature values extracted from the each image field local units that are associated with the each of the plurality of anomaly results, and to learn each of a plurality of weights for each of the anomaly results as a function of a relation of their normalized values of the extracted image features to the clustered image feature values extracted from the each respective associated image field local units; and fourth program instructions to multiply the normalized values of extracted features of each of the plurality of anomalies by their respective learned weights to generate respective ranking values and rank the plurality of anomalies by their generated respective ranking values; and wherein the first, second, third and fourth program instructions are stored on the computer readable storage medium for execution by the processing unit via the computer readable memory. - View Dependent Claims (9, 10, 11, 12)
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13. An article of manufacture, comprising:
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a computer readable storage medium having computer readable program code embodied therewith, the computer readable program code comprising instructions that, when executed by a computer processor, cause the computer processor to; retrieve a plurality of anomaly results from an anomaly detection module comprising a plurality of local models in response to an input query for an anomaly, wherein the anomaly detection module local models comprise image feature values extracted from an image field of video image data with respect to each of a plurality of different predefined spatial and temporal local units, and each of the plurality of anomaly results are determined by respective failures to fit to applied ones of the anomaly detection module local models; normalize each of a plurality of values of image features extracted from the image field local units and that are associated with each of the plurality of anomaly results; cluster image feature values extracted from the each image field local units that are associated with the each of the plurality of anomaly results; learn each of a plurality of weights for each of the anomaly results as a function of a relation of their normalized values of the extracted image features to the clustered image feature values extracted from the each respective associated image field local units; multiply the normalized values of extracted features of each of the plurality of anomalies by their respective learned weights to generate respective ranking values; and rank the plurality of anomalies by their generated respective ranking values. - View Dependent Claims (14, 15, 16, 17, 19, 20, 21, 22)
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18. A method for providing a service for using models learned from anomaly detection to rank detected anomalies, the method comprising:
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providing a results retriever that retrieves a plurality of anomaly results from an anomaly detection module comprising a plurality of local models in response to an input query for an anomaly, wherein the anomaly detection module local models comprise image feature values extracted from an image field of video image data with respect to each of a plurality of different predefined spatial and temporal local units, and each of the plurality of anomaly results are determined by respective failures to fit to applied ones of the anomaly detection module local models; providing a priority learning component that normalizes each of a plurality of values of image features extracted from the image field local units and that are associated with each of the plurality of anomaly results, clusters image feature values extracted from the each image field local units that are associated with the each of the plurality of anomaly results, learns each of a plurality of weights for each of the anomaly results as a function of a relation of their normalized values of the extracted image features to the clustered image feature values extracted from the respective associated each image field local units; and provides a ranker that multiplies the normalized values of extracted features of each of the plurality of anomalies by their respective learned weights to generate respective ranking values and ranks the plurality of anomalies by their generated respective ranking values.
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Specification