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Abstract-This
paper presents the comparative analysis of texture image classification using
three methods. Texture is a repeating pattern of local variation in image
intensity. The texture provides information in the spatial arrangement of
colors or intensities in an image, thus texture is a feature used to partition
images into regions of interest and to classify those region. The
content based image retrieval technique (CBIR) is very effective if
classification of large scale general purpose image database into textured and
non textured images is done. A technique to accurately classify the images into
textured or non textured category is based on image features. In this paper we
present three methods comparison purpose for classification of textured image.
The third method is proposed method based on neural network method excepting
that gives better accuracy for image classification.

Keywords- Textured image, Support Vector Machines, Grey Level, Image
segmentation, Wavelet transforms.

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1.Introduction-Texture
classification is important in content based image retrieval (CBIR) system. The
CBIR is technique for retrieving semantically relevant images from an image
database based on automatically derived image features. Texture classification
is concerned with identifying given textured region from given set of textured
classes. The texture classification is basically classifying pixels in an image
according to their texture cues .Three principles approaches used in image
processing to describe the texture of region are  Statistical ,Spectral, Support Vector Machines
In this paper the following three methods were discussed

1.      
Classification of
image using color and texture attributes.

2. Texture Image
Classification Using Support Vector Machine

3. Texture
Classification Based on Neural network and wavelet Transform

 

2.Classification of image using
color and texture attributes.-In this method we
propose an algorithm to improve the accuracy of this classification by
employing wavelet transform for extraction of feature for monochrome as well as
color images. We use an algorithm to
classify a Photographic image as textured and non textured, using region
segmentation and statistical testing. 1

The algorithm uses well known  LUV color space where L encodes frequency
information (luminance). U and v encodes color information (chrominance).To
obtain remaining three feature the Harr wavelet transform is applied to the L
component of the image.7 The k-means algorithm is used to cluster the feature
vectors into several classes with every class corresponding to one region in
the segmented image. The k-means algorithm is a well-known statistical
classification algorithm The k-means algorithm is used to cluster the feature
vectors into several classes with every class corresponding to one region in
the segmented image. K-mean algorithm uses pixel wise segmentation instead of
block wise segmentation.3 After applying K-means clustering algorithm we
obtain different classes. To classify images into the semantic classes textured
or non-textured, a mathematical description of how evenly a region scatters in
an image is the goodness of match between the distribution of the region and a
uniform distribution. The goodness of fit is measured by the x2 statistics.

Textured and non-textured images
are classified by thresholding the average x2 statistics for all the regions in
the image.

                                      m 

    
                 X2= 1/m ? xi2                                       2.1

                                    i=1                     

Where  Xi2 —- statistics in region i (i= 1 …m)

               X2 — average statistics 

 Where Xi2 
=

   
                2.2

                                       

If  X 2

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