Visual event classification with human like perception

The primary objective of automated motion semantic classification would be to recognizing events in close similarity with human like perception. This work proposes novel modifications to the standard spectral clustering algorithm in enhancing its capacity to capture human like semantics for visual event classification. The proposed novel multi-feature aggregation strategy replicates human like decision making, incorporating the contextual information of features rather than attempting blind fusion of them. The structural alterations introduced in the Laplacian enabled the methodology to alter the scheme of an event to be detected as an anomalous activity similar to human interpretation. Results of the implemented methodology have been demonstrated for experiments conducted on video streams focusing on human motion patterns.