publication . Conference object . 2016

Online multi-task learning for semantic concept detection in video

Foteini Markatopoulou; Vasileios Mezaris; Ioannis Patras;
Open Access English
  • Published: 25 Sep 2016
  • Publisher: IEEE
Abstract
In this paper we propose an online multi-task learning algorithm for video concept detection. In particular, we extend the Efficient Lifelong Learning Algorithm (ELLA) in the following ways: a) we solve the objective function of ELLA using quadratic programming instead of solving the Lasso problem, b) we add a new label-based constraint that considers concept correlations, c) we use linear SVMs as base learners instead of logistic regression. Experimental results show improvement over both the single-task learning methods typically used in this problem and the original ELLA algorithm.
Persistent Identifiers
Subjects
free text keywords: Concept detection, Video, Multi-task learning, Linear programming, Semantics, Lasso (statistics), Artificial intelligence, business.industry, business, Feature extraction, Support vector machine, Quadratic programming, Lifelong learning, Machine learning, computer.software_genre, computer, Computer science
Funded by
EC| InVID
Project
InVID
In Video Veritas – Verification of Social Media Video Content for the News Industry
  • Funder: European Commission (EC)
  • Project Code: 687786
  • Funding stream: H2020 | IA
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