3 research outputs found
Learning to detect video events from zero or very few video examples
In this work we deal with the problem of high-level event detection in video.
Specifically, we study the challenging problems of i) learning to detect video
events from solely a textual description of the event, without using any
positive video examples, and ii) additionally exploiting very few positive
training samples together with a small number of ``related'' videos. For
learning only from an event's textual description, we first identify a general
learning framework and then study the impact of different design choices for
various stages of this framework. For additionally learning from example
videos, when true positive training samples are scarce, we employ an extension
of the Support Vector Machine that allows us to exploit ``related'' event
videos by automatically introducing different weights for subsets of the videos
in the overall training set. Experimental evaluations performed on the
large-scale TRECVID MED 2014 video dataset provide insight on the effectiveness
of the proposed methods.Comment: Image and Vision Computing Journal, Elsevier, 2015, accepted for
publicatio
Deliverable D7.5 LinkedTV Dissemination and Standardisation Report v2
This deliverable presents the LinkedTV dissemination and standardisation report for the project period of months 19 to 30 (April 2013 to March 2014)
Deliverable D9.3 Final Project Report
This document comprises the final report of LinkedTV. It includes a publishable summary, a plan for use and dissemination of foreground and a report covering the wider societal implications of the project in the form of a questionnaire