Journal Article10.1109/34.481543
Learning texture discrimination masks
Anil K. Jain,Kalle Karu +1 more
216
TL;DR: A neural network texture classification method is proposed that is introduced as a generalization of the multichannel filtering method, and successfully applied in the tasks of locating barcodes in the images and segmenting a printed page into text, graphics, and background.
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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. The authors 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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Citations
Filtering for texture classification: a comparative study
Trygve Randen,John Hakon Husoy +1 more
TL;DR: Most major filtering approaches to texture feature extraction are reviewed and a ranking of the tested approaches based on extensive experiments is presented, showing the effect of the filtering is highlighted, keeping the local energy function and the classification algorithm identical for most approaches.
1.6K
Representing shape with a spatial pyramid kernel
Anna Bosch,Andrew Zisserman,X. Munoz +2 more
- 09 Jul 2007
TL;DR: This work introduces a descriptor that represents local image shape and its spatial layout, together with a spatial pyramid kernel that is designed so that the shape correspondence between two images can be measured by the distance between their descriptors using the kernel.
1.6K
Outex - new framework for empirical evaluation of texture analysis algorithms
Timo Ojala,Topi Mäenpää,Matti Pietikäinen,J. Viertola,J. Kyllonen,S. Huovinen +5 more
- 11 Aug 2002
TL;DR: The proposed Outex framework contains a large collection of surface textures captured under different conditions, which facilitates construction of a wide range of texture analysis problems.
757
A Statistical Approach to Material Classification Using Image Patch Exemplars
Manik Varma,Andrew Zisserman +1 more
TL;DR: It is demonstrated that materials can be classified using the joint distribution of intensity values over extremely compact neighborhoods (starting from as small as 3times3 pixels square) and that this can outperform classification using filter banks with large support.
Support vector machines for texture classification
TL;DR: Experimental results demonstrate the effectiveness of SVMs in texture classification, and it is shown that SVMs can incorporate conventional texture feature extraction methods within their own architecture, while also providing solutions to problems inherent in these methods.
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