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.