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Learning Texture Discrimination Masks
February 1996 (vol. 18 no. 2)
pp. 195-205

Abstract—A neural network texture classification method is proposed in this paper. The approach is introduced as a generalization of the multichannel filtering method. Instead of using a general filter bank, a neural network is trained to find a minimal set of specific filters, so that both the feature extraction and classification tasks are performed by the same unified network. We compute the error rates for different network parameters, and show the convergence speed of training and node pruning algorithms. The proposed method is demonstrated in several texture classification experiments. It is successfully applied in the tasks of locating barcodes in the images and segmenting a printed page into text, graphics, and background. Compared with the traditional multichannel filtering method, the neural network approach allows one to perform the same texture classification or segmentation task more efficiently. Extensions of the method, as well as its limitations, are discussed in the paper.

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Index Terms:
Texture, segmentation, learning, neural networks, feature extraction.
Citation:
Anil K. Jain, Kalle Karu, "Learning Texture Discrimination Masks," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, no. 2, pp. 195-205, Feb. 1996, doi:10.1109/34.481543
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