Semantic event detection using cross-domain knowledge

Abstract

A method for facilitating semantic event classification of a group of image records related to an event. The method utilizing an event detector system for providing: extraction of a plurality of visual features from each of the image records; generation of a plurality of concept scores for each of the image records using the visual features; generation of a feature vector corresponding to the event based on the concept scores of the image records; and supplying of the feature vector to an event classifier that identifies at least one semantic event classifier that corresponds to the event, wherein the visual features include segmenting an image record into a number of regions, in which the visual features are extracted, each concept score corresponds to a visual concept and each concept score is indicative of a probability that the image record includes the visual concept.

Description

[0001] This application is a divisional application of the patent application with the application number 201080012880.4 and the invention title "Semantic event detection using cross-domain knowledge", which was submitted to the State Intellectual Property Office on September 20, 2011. technical field [0002] The present invention relates to classifying digital content recordings, such as digital still images or video. In particular, the present invention relates to the classification of digital content records based on semantic event detection. Background technique [0003] The advent of low-cost imaging technology for consumer electronics has resulted in a significant increase in the number of digital images captured by the average user. In fact, as various forms of electronic storage have become less expensive over time, users have tended to capture more digital still images and videos and retain digital still images and videos that they would otherwise discard. Thus, t...

AI Technical Summary

Problems solved by technology

However, there are fundamental problems with semantic event detection as follows: first, practical systems need to be able to handle both digital still images and videos, since both digital still images and videos usually exist in the image corpora of real users; second, practical systems need to accommodate real different semantic content in the user corpus, thus making it ideal to provide systems that include general methods for detecting different semantic events rather than specific individual methods for detecting each specific semantic event; finally, practical systems need to be robust against Errors in identification and classification

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