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Proceedings Paper

Swarm optimization methods for cognitive image analysis
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Paper Abstract

We describe cognitive swarms, a new method for efficient visual recognition of objects in an image or video sequence that combines feature-based object classification with search mechanisms based on swarm intelligence. Our approach utilizes the particle swarm optimization algorithm (PSO), a population based evolutionary algorithm, which is effective for optimization of a wide range of functions. PSO searches a multi-dimensional solution space for a global optimum using a population or swarm of "particles" that cooperate using a low overhead communication scheme to search the solution space efficiently. We use a system of local and global swarms to detect and track multiple objects in video sequences. In our implementation, each particle in the swarm consists of a cascade of classifiers that utilize wavelet and edge-symmetry features to recognize objects. PSO update equations are used to control the movement of the swarm in solution space as the particles cooperate to find objects efficiently by maximizing classification confidence. By performing this optimization, the classifier swarm finds objects in the scene, determines their size, and optimizes other classifier parameters such as the object rotation angle. Map-based attention feedback is used to further increase the efficiency of cognitive swarms. Performance results are presented for human and vehicle detection.

Paper Details

Date Published: 27 September 2007
PDF: 12 pages
Proc. SPIE 6712, Unconventional Imaging III, 67120K (27 September 2007); doi: 10.1117/12.747438
Show Author Affiliations
Yuri Owechko, HRL Labs., LLC (United States)
Swarup Medasani, HRL Labs., LLC (United States)

Published in SPIE Proceedings Vol. 6712:
Unconventional Imaging III
Jean J. Dolne; Victor L. Gamiz; Paul S. Idell, Editor(s)

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