System and method for relevance estimation in summarization of videos of multi-step activities

A system for identifying content relevance, said system comprising:

• a video acquisition module for acquiring video data;
• a processor;
• a data bus coupled to said processor;
• and
• a computer-usable medium embodying computer program code, said computer-usable medium being coupled to said data bus, said computer program code comprising instructions executable by said processor and configured for:
• mapping said acquired video data to a feature space to obtain a feature representation of said video data;
• assigning said acquired video data, via the use of a classifier, to at least one action class based on said feature representation of said video data;
• and
• determining a relevance of said acquired video data based on said classifier output.

A processor-readable medium storing computer code representing instructions to cause a process for identifying content relevance, said computer code comprising code to:

• train a classifier to optimally discriminate between a plurality of different action classes according to said feature representations;
• and
• in an online stage:
• acquire video data said video data comprising one of video acquired with an egocentric or wearable device;
• video acquired with a vehicle-mounted device;
• and surveillance or third-person view video;
• segment said video data into at least one of a series of single frames and a series of groups of frames;
• map said acquired video data to a feature space to obtain a feature representation of said video data;
• assign said acquired video data, via the use of a classifier, to at least one action class based on said feature representation of said video data;
• and
• assign said acquired data a classification confidence score and convert said classification confidence score to a relevance score to determine a relevance of said acquired video data based on the classifier output.