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Proceedings Paper

A model-based conceptual clustering of moving objects in video surveillance
Author(s): Jeongkyu Lee; Pragya Rajauria; Subodh Kumar Shah
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Paper Abstract

Data mining techniques have been applied in video databases to identify various patterns or groups. Clustering analysis is used to find the patterns and groups of moving objects in video surveillance systems. Most existing methods for the clustering focus on finding the optimum of overall partitioning. However, these approaches cannot provide meaningful descriptions of the clusters. Also, they are not very suitable for moving object databases since video data have spatial and temporal characteristics, and high-dimensional attributes. In this paper, we propose a model-based conceptual clustering (MCC) of moving objects in video surveillance based on a formal concept analysis. Our proposed MCC consists of three steps: 'model formation', 'model-based concept analysis', and 'concept graph generation'. The generated concept graph provides conceptual descriptions of moving objects. In order to assess the proposed approach, we conduct comprehensive experiments with artificial and real video surveillance data sets. The experimental results indicate that our MCC dominates two other methods, i.e., generality-based and error-based conceptual clustering algorithms, in terms of quality of concepts.

Paper Details

Date Published: 29 January 2007
PDF: 12 pages
Proc. SPIE 6506, Multimedia Content Access: Algorithms and Systems, 650602 (29 January 2007); doi: 10.1117/12.708229
Show Author Affiliations
Jeongkyu Lee, Univ. of Bridgeport (United States)
Pragya Rajauria, Univ. of Bridgeport (United States)
Subodh Kumar Shah, Univ. of Bridgeport (United States)


Published in SPIE Proceedings Vol. 6506:
Multimedia Content Access: Algorithms and Systems
Alan Hanjalic; Raimondo Schettini; Nicu Sebe, Editor(s)

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