An Automated Video Object Extraction System Based on Spatiotemporal Independent Component Analysis and Multiscale Segmen
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An Automated Video Object Extraction System Based on Spatiotemporal Independent Component Analysis and Multiscale Segmentation Xiao-Ping Zhang and Zhenhe Chen Department of Electrical and Computer Engineering, Faculty of Engineering and Applied Science, Ryerson University, 350 Victoria Street, Toronto, ON, Canada M5B 2K3 Received 12 September 2004; Revised 13 March 2005; Accepted 27 May 2005 Video content analysis is essential for efficient and intelligent utilizations of vast multimedia databases over the Internet. In video sequences, object-based extraction techniques are important for content-based video processing in many applications. In this paper, a novel technique is developed to extract objects from video sequences based on spatiotemporal independent component analysis (stICA) and multiscale analysis. The stICA is used to extract the preliminary source images containing moving objects in video sequences. The source image data obtained after stICA analysis are further processed using wavelet-based multiscale image segmentation and region detection techniques to improve the accuracy of the extracted object. An automated video object extraction system is developed based on these new techniques. Preliminary results demonstrate great potential for the new stICA and multiscale-segmentation-based object extraction system in content-based video processing applications. Copyright © 2006 Hindawi Publishing Corporation. All rights reserved.
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INTRODUCTION
The increasing popularity of video processing is due to the high demand for video in entertainment, security related applications, education, telemedicine, database, and new wireless telecommunications. Recently, interesting research topics such as automated and efficient content-based video processing techniques are attracting much attention. The content-based video presentation is an essential need for emerging broadcasting services, Internet, and security applications. Raw video clips are usually binary streams that are not well organized. To represent their contents, video clips must be decomposed into objects so analysis can be performed. The object-based technique is one way of analyzing the video clips and it is gaining importance in achieving compression and performing content-based video retrieval. Recently, partitioning video sequences into semantic video objects has been an active research area. Applications to object-based video representation include video conference, biomedical, surveillance, and content-based video indexing and retrieval. Video coding standard MPEG-4 also introduces an object-based framework for multimedia representation [1]. To maximize the benefit of the industry standard and to provide object-level multimedia interaction,
automatic video object segmentation techniques need to be developed. Classical solutions to video object segmentation are based on motion features. A technique to represent video in layers was proposed in [2]. Image sequence is decomposed into layers by estimating and clustering affine parameters. Borshukov et al.[3]
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