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Standard Performance Evaluation Corporation

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SPEC CPU2000 Benchmark Description File

Benchmark Name

Benchmark Author

Charles Roberson & Max Domeika

Benchmark Program General Category

Image Recognition/Neural networks

Benchmark Description

The Adaptive Resonance Theory 2 (ART 2) neural network is used to recognize objects in a thermal image. The objects are a helicopter and an airplane. The neural network is first trained on the objects. After training is complete, the learned images are found in the scanfield image. A window corresponding to the size of the learned objects is scanned across the scanfield image and serves as input for the neural network. The neural network attempts to match the windowed image with one of the images it has learned.

Description of ART 2

The ART 2 neural network models several characteristics of organic neural processing that is not modelled in more traditional Feed Forward Neural Networks(FFNN). In brief, ART 2 neural networks offer the following advantages over traditional FFNN:

  • Expectation influences inputs - The past learnings of an ART 2 neural network influence the matching process.
  • Creates own classifications - During training, the ART 2 neural network does not need explicit output information; it creates its own classification groups.
  • Learns on-the-fly - ART 2 neural networks are capable of learning and classifying at the same time. The benchmark does not use this feature of ART 2 neural networks.
  • Contrast enhancement - ART 2 neural networks perform constrast enhancement through a series of normalizations in the dynamical system.

Input Description

The training files consist of a thermal image of a helicopter and an airplane. The scanfile is a field of view containing other thermal views of the helicopter and airplane.

Output Description

The output data consists of the confidence of a match between the learned image and the windowed field of view. In addition, each F2 neuron's output is printed. After the entire field of view is scanned the field of view with the highest confidence of being a match is output.

Programming Language


Known portability issues



C. W. Roberson, "Design Extensions To Adaptive Resonance Theory Neural Networks," Master's Project, Clemson University(1994).

M.J. Domeika, C.W. Roberson, E.W. Page, and G.A. Tagliarini, "Adaptive Resonance Theory 2 Neural Network Approach To Star Field Recognition," in Applications and Science of Artificial Neural Networks II, Steven K. Rogers, Dennis W. Ruck, Editors, Proc. SPIE 2760, pp. 589-596(1996).

G.A. Carpenter and S. Grossberg, "ART 2: Stable self-organization of pattern recognition codes for analog input patterns," Applied Optics, 26, pp. 4919-4930(1987).

Last Updated: 12 October 1999