  Texture segmentation is the process of partitioning an image into regions
with different textures containing a similar group of pixels. Detecting the
discontinuity of the filter's output and their statistical properties help in
segmenting and classifying a given image with different texture regions. In
this proposed paper, chili x-ray image texture segmentation is performed by
using Gabor filter. The texture segmented result obtained from Gabor filter fed
into three texture filters, namely Entropy, Standard Deviation and Range
filter. After performing texture analysis, features can be extracted by using
Statistical methods. In this paper Gray Level Co-occurrence Matrices and First
order statistics are used as feature extraction methods. Features extracted
from statistical methods are given to Support Vector Machine (SVM) classifier.
Using this methodology, it is found that texture segmentation is followed by
the Gray Level Co-occurrence Matrix feature extraction method gives a higher
accuracy rate of 84% when compared with First order feature extraction method.
  Key Words: Texture segmentation, Texture filter, Gabor filter, Feature
extraction methods, SVM classifier.
