By Topic

A semi-supervised incremental learning framework for sports video view classification

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Jun Wu ; Dept. of Comput. Sci. & Technol., Tsinghua Univ., Beijing ; Bo Zhang ; Xian-Sheng Hua ; Jianwei Zhang

Sports videos have special characteristics such as well-defined video structure, specialized sports syntax, and typically having some canonical view types. In this paper, we propose a semi-supervised incremental learning framework for sports video view classification. Baseball is selected as an example to explain the main ideas. In order to obtain an optimal model based on a small number of pre-labeled training samples, the semi-supervised incremental learning framework explores the local distributed properties of the video sequences and sufficiently utilizes the information of a positive model pool and a negative model pool. After each round of online optimization process for the under-investigating video, a locally-optimized positive model and a set of negative models are added into the positive model pool and the negative model pool according to some heuristic criteria, respectively. Experiments results on real sports video data show that the proposed system is effective and promising

Published in:

Multi-Media Modelling Conference Proceedings, 2006 12th International

Date of Conference:

0-0 0