A model-based conceptual clustering of moving objects in video surveillance
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Authors
Lee, Jeongkyu
Rajauria, Pragya
Shah, Subodh K.
Issue Date
2007-01-29
Type
Article
Language
en_US
Keywords
Conceptual clustering , Formal concept analysis , Expectation maximization , Moving objects
Alternative Title
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.
Description
Copyright 2007 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
Citation
Jeongkyu Lee ; Pragya Rajauria and Subodh K. Shah "A model-based conceptual clustering of moving objects in video surveillance", Proc. SPIE 6506, Multimedia Content Access: Algorithms and Systems, 650602 (January 29, 2007); doi: 10.1117/12.708229
Publisher
SPIE