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1 Foundations of Computing and Decision Sciences

1 2006

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Designing pruned neighborhood neural networks for object extraction from noisy background

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Bhattacharyya S. , Dutta P.

Foundations of Computing and Decision Sciences

2006

tom Vol. 31, No. 2

105-134

Refinement of neural network architectures by pruning the network interconnections reduces the computational overhead associated with the tasks for which the network is employed. A fuzzy set theoretic approach for designing pruned neighborhood topology-based neural networks for efficient extraction of objects from a noisy background, is presented in this paper. Pruning of the network architecture Is achieved by means of a judicious selection of the participating nodes of the neighborhood topology-based neural network using the fuzzy cardinality measures of the object scene. An application of the proposed methodology for designing a pruned multilayer self organizing neural network for the extraction of binary and gray scale objects from noisy backgrounds with different noise levels is demonstrated. The results obtained are compared with the outputs obtained with the conventional fully connected network architecture of the same network. Comparative results show a significant reduction in the architecture of the network with increasing noise levels for both the binary and gray scale images. Moreover, the qualities of the extracted images obtained using the pruned network architecture are found to be better than those obtained using the conventional fully connected architecture.