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Proceedings Paper

Spectral and bispectral feature-extraction neural networks for texture classification
Author(s): Keisuke Kameyama; Yukio Kosugi
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Paper Abstract

A neural network model (Kernel Modifying Neural Network: KM Net) specialized for image texture classification, which unifies the filtering kernels for feature extraction and the layered network classifier, will be introduced. The KM Net consists of a layer of convolution kernels that are constrained to be 2D Gabor filters to guarantee efficient spectral feature localization. The KM Net enables an automated feature extraction in multi-channel texture classification through simultaneous modification of the Gabor kernel parameters (central frequency and bandwidth) and the connection weights of the subsequent classifier layers by a backpropagation-based training rule. The capability of the model and its training rule was verified via segmentation of common texture mosaic images. In comparison with the conventional multi-channel filtering method which uses numerous filters to cover the spatial frequency domain, the proposed strategy can greatly reduce the computational cost both in feature extraction and classification. Since the adaptive Gabor filtering scheme is also applicable to band selection in moment spectra of higher orders, the network model was extended for adaptive bispectral filtering for extraction of the phase relation among the frequency components. The ability of this Bispectral KM Net was demonstrated in the discrimination of visually discriminable synthetic textures with identical local power spectral distributions.

Paper Details

Date Published: 14 October 1997
PDF: 11 pages
Proc. SPIE 3167, Statistical and Stochastic Methods in Image Processing II, (14 October 1997); doi: 10.1117/12.279651
Show Author Affiliations
Keisuke Kameyama, Tokyo Institute of Technology (Japan)
Yukio Kosugi, Tokyo Institute of Technology (Japan)


Published in SPIE Proceedings Vol. 3167:
Statistical and Stochastic Methods in Image Processing II
Francoise J. Preteux; Jennifer L. Davidson; Edward R. Dougherty, Editor(s)

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