Systems and methods for retrieving causal sets of events from unstructured signals

摘要

A method for providing improved performance in retrieving and classifying causal sets of events from an unstructured signal can comprise applying a temporal-causal analysis to the unstructured signal. The temporal-causal analysis can comprise representing the occurrence times of visual events from an unstructured signal as a set of point processes. An exemplary embodiment can comprise interpreting a set of visual codewords produced by a space-time-dictionary representation of the unstructured video sequence as the set of point processes. A nonparametric estimate of the cross-spectrum between pairs of point processes can be obtained. In an exemplary embodiment, a spectral version of the pairwise test for Granger causality can be applied to the nonparametric estimate to identify patterns of interactions between visual codewords and group them into semantically meaningful independent causal sets. The method can further comprise leveraging the segmentation achieved during temporal causal analysis to improve performance in categorizing causal sets.

主权项

1. A computer program product embodied in a non-transitory computer-readable medium, the computer program product comprising an algorithm adapted to effectuate a method for analyzing visual events comprising:
selecting a plurality of visual events in a visual recording, wherein a visual event is a local visual feature occurring over a plurality of video frames; for one or more occurrences of the plurality of visual events, representing an occurrence of a visual event as a point process, to create a plurality of point processes; constructing a non-parametric representation of the plurality of point processes; and identifying, between pairs of point processes, causal sets providing evidence of causal relationships between the pairs of point processes; wherein the non-parametric representation of the plurality of point processes is an estimate of a cross-spectral density function.