Book Chapter10.1007/978-3-642-57280-7_24
Automatic Texture Classification by Visual Properties
T. Hermes,A. Miene,O. Moehrke +2 more
- 01 Jan 2000
- pp 219-226
6
TL;DR: This paper provides a way of domain independent classification of textures by finding a direct mapping between certain statistical texture features and the items of the set of visual texture properties.
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Abstract: In this paper we provide an approach for automatic texture description based on visual texture properties. A set of texture features which directly coincide with the human visual perception of textures could be useful for e.g. domain independent texture classification in image retrieval systems like IRIS (Hermes et al. (1995)). Therefore, this set of items concerning the visual properties was tested on natural textures as well as on synthetic textures. Various statistical texture features were evaluated and we found a direct mapping between certain statistical texture features and the items of the set of visual texture properties. Therefore, we provide a way of domain independent classification of textures.
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References
Textural Features for Image Classification
Robert M. Haralick,K. Shanmugam,Its'hak Dinstein +2 more
- 01 Nov 1973
TL;DR: These results indicate that the easily computable textural features based on gray-tone spatial dependancies probably have a general applicability for a wide variety of image-classification applications.
23.6K
Textural Features Corresponding to Visual Perception
Hideyuki Tamura,Shunji Mori,Takashi Yamawaki +2 more
- 01 Jun 1978
TL;DR: The discrepancies between human vision and computerized techniques that are encountered in this study indicate fundamental problems in digital analysis of textures and could be overcome by analyzing their causes and using more sophisticated techniques.
2.5K
Texture analysis using gray level run lengths
TL;DR: In this paper, a set of texture features based on gray level run lengths is described, and good classification results are obtained with these features on a sets of samples representing nine terrain types.
2.2K
Texture analysis using grey level run lengths
M. M. Galloway
- 01 Jul 1974
TL;DR: A set of texture features based on gray level run lengths is described, and good classification results are obtained with these features on a set of samples representing nine terrain types.
1.7K
A comparative study of texture measures for terrain classification.
J. S. Weszka,A. Rosenfeld +1 more
- 01 Mar 1975
TL;DR: Three standard approaches to automatic texture classification make use of features based on the Fourier power spectrum, on second-order gray level statistics, and on first-order statistics of gray level differences, respectively; it was found that the Fouriers generally performed more poorly, while the other feature sets all performned comparably.
1.5K