NDLI: Motion Segmentation through Incremental Hierarchical Clustering

Content Provider	IEEE Xplore Digital Library
Author	Shah, S.A.A. Usman Naseem, M. Saif-ur-Rehman Karim, A.
Copyright Year	2006
Description	Author affiliation: Dept. of Comput. Sci., Lahore Univ. of Manage. Sci. (Shah, S.A.A.; Usman Naseem, M.; Saif-ur-Rehman; Karim, A.)
Abstract	Motion segmentation is a key step in many applications such as video surveillance, medical decision support, and target tracking. Motion segmentation is challenging because of the large amounts of data to be processed and the real-time requirements of the applications. The k-means clustering algorithm has often been used for motion segmentation. However, the k-means algorithm is computationally expensive and requires prior knowledge of the number of clusters. In this paper, we present an approach for motion segmentation based on the incremental hierarchical clustering algorithm BIRCH. BIRCH is scalable and efficient because it processes data incrementally and is more accurate because it does not require prior knowledge of clusters. We describe our experiments using video from a Web cam and compare the performance of BIRCH and k-means clustering for motion segmentation. Our results confirm that our approach is more accurate and efficient as compared to the k-means based approach.
Starting Page	134
Ending Page	139
File Size	6680819
Page Count	6
File Format	PDF
ISBN	142440794X
DOI	10.1109/INMIC.2006.358150
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-12-23
Publisher Place	Pakistan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Motion segmentation Computer vision Clustering algorithms Video surveillance Image segmentation Computer science Application software Target tracking Labeling Data preprocessing
Content Type	Text
Resource Type	Article

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