Image texture
An image texture is the small-scale structure perceived on an image, based on the spatial arrangement of color or intensities.
[1] It can be quantified by a set of metrics calculated in image processing.
For more accurate segmentation the most useful features are spatial frequency and an average grey level.
A structured approach sees an image texture as a set of primitive texels in some regular or repeated pattern.
A statistical approach sees an image texture as a quantitative measure of the arrangement of intensities in a region.
In general this approach is easier to compute and is more widely used, since natural textures are made of patterns of irregular subelements.
The use of edge detection is to determine the number of edge pixels in a specified region, helps determine a characteristic of texture complexity.
the gradient-based edge detector is applied to this region by producing two outputs for each pixel p: the gradient magnitude Mag(p) and the gradient direction Dir(p).
Hmag(R) denotes the normalized histogram of gradient magnitudes of region R, and Hdir(R) denotes the normalized histogram of gradient orientations of region R. Both are normalized according to the size NR Then
is a quantitative texture description of region R. The co-occurrence matrix captures numerical features of a texture using spatial relations of similar gray tones.
[3] Numerical features computed from the co-occurrence matrix can be used to represent, compare, and classify textures.
The following are a subset of standard features derivable from a normalized co-occurrence matrix:
th entry in a gray-tone spatial dependence matrix, and Ng is the number of distinct gray-levels in the quantized image.
One negative aspect of the co-occurrence matrix is that the extracted features do not necessarily correspond to visual perception.
Another approach is to use local masks to detect various types of texture features.
Laws[4] originally used four vectors representing texture features to create sixteen 2D masks from the outer products of the pairs of vectors.
The four vectors and relevant features were as follows: To these 4, a fifth is sometimes added:[5] From Laws' 4 vectors, 16 5x5 "energy maps" are then filtered down to 9 in order to remove certain symmetric pairs.
The resulting 9 maps used by Laws are as follows:[6] Running each of these nine maps over an image to create a new image of the value of the origin ([2,2]) results in 9 "energy maps," or conceptually an image with each pixel associated with a vector of 9 texture attributes.
The autocorrelation function of an image can be used to detect repetitive patterns of textures.
The use of image texture can be used as a description for regions into segments.
Though image texture is not a perfect measure for segmentation it is used along with other measures, such as color, that helps solve segmenting in image.
Attempts to group or cluster pixels based on texture properties.
Peter Howarth, Stefan Rüger, "Evaluation of texture features for content-based image retrieval", Proceedings of the International Conference on Image and Video Retrieval, Springer-Verlag, 2004 A detailed description of texture analysis in biomedical images can be found in Depeursinge et al.
