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Video object segmentation and tracking using 2-learning classification

By Yi Liu, Student Member and Yuan F. Zheng

Abstract

Abstract—As a requisite of the emerging content-based multimedia technologies, video object (VO) extraction is of great importance. This paper presents a novel semiautomatic segmentation and tracking method for single VO extraction. Unlike traditional approaches, the proposed method formulates the separation of the VO from the background as a classification problem. Each frame is divided into small blocks of uniform size, which are called object blocks if the centering pixels belong to the object, or background blocks otherwise. After a manual segmentation of the first frame, the blocks of this frame are used as the training samples for the object-background classifier. A newly developed learning tool called-learning is employed to train the classifier which outperforms the conventional Support Vector Machines in linearly nonseparable cases. To deal with large and complex objects, a multilayer approach constructing a so-called hyperplan

Topics: IN THE PAST severa
Year: 2005
OAI identifier: oai:CiteSeerX.psu:10.1.1.135.1690
Provided by: CiteSeerX
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