Swarm optimization methods for cognitive image analysis

Yuri Owechko; Swarup Medasani

doi:10.1117/12.747438

27 September 2007 Swarm optimization methods for cognitive image analysis

Yuri Owechko, Swarup Medasani

Proceedings Volume 6712, Unconventional Imaging III; 67120K (2007) https://doi.org/10.1117/12.747438
Event: Optical Engineering + Applications, 2007, San Diego, California, United States

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.

Citation Download Citation

Yuri Owechko and Swarup Medasani "Swarm optimization methods for cognitive image analysis", Proc. SPIE 6712, Unconventional Imaging III, 67120K (27 September 2007); https://doi.org/10.1117/12.747438

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available