Texture feature extraction

doi:10.1016/0167-8655(87)90087-0

Pattern Recognition Letters

Volume 6, Issue 4, September 1987, Pages 269-273

https://doi.org/10.1016/0167-8655(87)90087-0 Get rights and content

Abstract

We present a new approach to texture feature extraction from a cooccurrence matrix. Computationally, the method is much faster than traditional uses of cooccurrence matrices. Using Brodatz's textures, the proposed features are evaluated and compared with those suggested by Conners et al. (1984).

References (10)

R.W. Conners et al.
Segmentation of a high-resolution urban scence using texture operators
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C.H. Chen
A study of texture classification using spectral features
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Utilisation des données landsat pour la cartographie des formations végétales tropicales dans le sud de Sumatra
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Utilisatiotn des données landsat pour la catographie des formations végétales tropicales dans le sud de Sumatra
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Cited by (34)

Off-line signature verification based on grey level information using texture features
2011, Pattern Recognition
Citation Excerpt :
The grey level co-occurrence matrix (GLCM) method is a way of extracting second order statistical texture features from the image [31]. This approach has been used in a number of applications, including ink type analysis [16], e.g. [32–34]. For a statistically reliable estimation of the relative frequency we need a sufficiently large number of occurrences for each event.
A method for conducting off-line handwritten signature verification is described. It works at the global image level and measures the grey level variations in the image using statistical texture features. The co-occurrence matrix and local binary pattern are analysed and used as features. This method begins with a proposed background removal. A histogram is also processed to reduce the influence of different writing ink pens used by signers. Genuine samples and random forgeries have been used to train an SVM model and random and skilled forgeries have been used for testing it. Results are reasonable according to the state-of-the-art and approaches that use the same two databases: MCYT-75 and GPDS-100 Corpuses. The combination of the proposed features and those proposed by other authors, based on geometric information, also promises improvements in performance.
Neural networks for the classification of image texture
1994, Engineering Applications of Artificial Intelligence
In this paper, an extensive study of the efficiency of secondary features extracted from co-occurrence matrices and then direct use of co-occurrence matrices for the classification of texture images is carried out. First, the classification capabilities of individual, and then several combinations of, secondary features extracted from the co-occurrence matrices are examined. An adequate combination consisting of four of those features is hence established. A feed-forward artificial neural network is used as a classifier. The classification performance of the network as a function of the training data volume, its size and the training strategy are investigated. Next, an investigation is made into the direct use of co-occurrence matrix entries for classification. A simple technique, where the gray levels in the original images are grouped into a small number of equal bands, is employed in order to reduce the size of the matrix. It is shown that results obtained here compare favorably with those obtained earlier using the set of secondary features. The efficiency of the last technique is further demonstrated by means of considering a practical application. This involves the surface texture classification of wear particles found in the lubricating oil of moving machinery and engines. Again, it is shown that high recognition rates can be achieved when dealing with such classification tasks.
Statistical feature matrix for texture analysis
1992, CVGIP: Graphical Models and Image Processing
A new approach using the statistical feature matrix, which measures the statistical properties of pixel pairs at several distances, within an image, is proposed for texture analysis. The major properties of this approach are that (1) the size of the matrix is dependent on the maximum distance used instead of the number of gray-levels, (2) the matrix can be expanded easily and (3) some physical properties can be evaluated from the matrix. These properties have enhanced the practical applications of the matrix. In this paper, the matrix is applied to texture classification and visual-perceptual feature extraction. For texture classification, two experiments are performed. First, 16 Brodatz textures are employed to evaluate the performance of the matrix. A simple distance measure is defined to determine the similarity between two statistical feature matrices. Texture discrimination in an additive noise environment is also considered. Second, we apply the matrix to the classification of 150 sampled ultrasonic liver images. From experimental results it can be found that our approach is better than the spatial gray-level dependence method and the spatial frequency-based method. For visual-perceptual feature extraction, we evaluate five basic texture features, namely, coarseness, contrast, regularity, periodicity and roughness, from the statistical feature matrix. It is shown that the statistical feature matrix is an excellent tool for texture analysis.
Detecting texture edges from images
1992, Pattern Recognition
Edge detection takes an important place in image processing and pattern recognition. Its objective is to locate prominent edges in an image, and so to separate the components of an image into subsets that may correspond to the physical objects in the scene. In general, this can be achieved by generating an edge map using, for example, edge detection operators or some threshold techniques. In most cases, these methods assume that at the edge the grey level intensity changes in a discontinuous way (usually as a step function). If we need to segment a textural scene by finding the texture boundaries, traditional methods of edge detection are usually not successful since they cannot distinguish between the micro-edges within each texture and the boundaries between different textures. One reason for their failure is their inability to properly characterize a texture. This problem can be solved by combining the traditional edge detection techniques with some efficient textural measures. That is, in the intensity based edge detection operators, grey levels are replaced by textural features. Recently, the texture spectrum method has been proposed for texture characterization. This paper presents an example of the application of the texture spectrum to edge detection. Promising results are obtained when locating texture boundaries of some of Brodatz's natural images.
Unsupervised textural classification of images using the texture spectrum
1992, Pattern Recognition
Pursuing our previous study (Wang and He, Pattern Recognition23, 905–910 (1990)) where the texture spectrum was proposed for a supervised classification of texture images, an example is presented here of the application of the texture spectrum to an unsupervised textural classification of images. Promising results are obtained when using the algorithm to classify six of Brodatz's natural texture images, demonstrating further the discrimination performance of the texture spectrum.
Textural filters based on the texture spectrum
1991, Pattern Recognition
Conventional digital filtering techniques, based on classical Fourier analysis (that is, low-pass, high-pass and band-pass), are widely used in digital image processing. Unsatisfaction may be encountered when applying these filters to texture analysis of images, where one needs some specific spatial filters which are able to transform an image in the sense of texture rather than the spectral properties. Such textural filters can be designed in the texture spectrum domain and they are of interest for texture analysis. An example is given in this paper, and has been applied to four of Brodatz's natural images. The result shows a promising potential of the texture spectrum for designing textural filters.

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Texture feature extraction

Abstract

International Journal of Remote Sensing

Textures: A photographic album for artists and designers

A study of texture classification using spectral features

Utilisation des données landsat pour la cartographie des formations végétales tropicales dans le sud de Sumatra

L'Espace Géographique

Utilisatiotn des données landsat pour la catographie des formations végétales tropicales dans le sud de Sumatra

L'Espace Géographique