As increasing levels of multimedia data online require more sophisticated methods to organise this data, we present a practical system for performing rapid localisation and retrieval of human actions from large video databases. We first temporally segment the database and calculate a histogram-match score for each segment against the query. High-scoring, adjacent segments are joined into candidate localised regions using a noise-robust localisation algorithm, and each candidate region is then ranked against the query. Experiments show that this method surpasses the efficiency of previous attempts to perform similar action searches with localisation. We demonstrate how results can be enhanced using relevance feedback, considering how relevance feedback can be effectively applied in the context of localisation.
|Publication status||Published - Aug 2013|
|Event||CAIP 2013 - 15th International Conference on Computer Analysis of Images and Patterns - York, UK|
Duration: 1 Aug 2013 → …
|Conference||CAIP 2013 - 15th International Conference on Computer Analysis of Images and Patterns|
|Period||1/08/13 → …|