07-09-2012, 12:19 PM
Image segmentation by Clustering
[attachment=33614]
ABSTRACT:
In this work the way is described an algorithm, which are used to segmentation of images with clustering methods. This algorithm is tested on ten color images, which are firstly transformed to R*B*G* color space. Conditions, results and conclusions are described lower. The results are compared using both Mahalanobis and Euclidean distances in the clustering algorithm.
INTRODUCTION:
Image segmentation was, is and will be a major research topic for many image processing researchers. The reasons are obvious and applications endless: most computer vision and image analysis problems require a segmentation stage in order to detect objects or divide the image into regions which can be considered homogeneous according to a given criterion, such as color, motion, texture, etc.
Clustering is the search for distinct groups in the feature space. It is expected that these groups have different structures and that can be clearly differentiated. The clustering task separates the data into number of partitions, which are volumes in the n-dimensional feature space. These partitions define a hard limit between the different groups and depend on the functions used to model the data distribution.
Image segmentation:
To humans, an image is not just a random collection of pixels; it is a meaningful arrangement of regions and objects. There also exits a variety of images: natural scenes, paintings, etc. Despite the large variations of these images, humans have no problem to interpret them. Considering the large databases on the WWW, in our personal photograph folders, a strong and automatic image analysis would be welcome.
Image Segmentation by Clustering
Clustering is a classification technique. Given a vector of N measurements describing each pixel or group of pixels (i.e., region) in an image, a similarity of the measurement vectors and therefore their clustering in the N-dimensional measurement space implies similarity of the corresponding pixels or pixel groups. Therefore, clustering in measurement space may be an indicator of similarity of image regions, and may be used for segmentation purposes.
The vector of measurements describes some useful image feature and thus is also known as a feature vector. Similarity between image regions or pixels implies clustering (small separation distances) in the feature space. Clustering methods were some of the earliest data segmentation techniques to be developed.
The K-Means Algorithm Process
• The dataset is partitioned into K clusters and the data points are randomly assigned to the clusters resulting in clusters that have roughly the same number of data points.
• For each data point:
• Calculate the distance (Mahalanobis or Euclidean) from the data point to each cluster.
• If the data point is closest to its own cluster, leave it where it is. If the data point is not closest to its own cluster, move it into the closest cluster.
• Repeat the above step until a complete pass through all the data points results in no data point moving from one cluster to another. At this point the clusters are stable and the clustering process ends.
• The choice of initial partition can greatly affect the final clusters that result, in terms of inter-cluster and intra-cluster distances and cohesion.
CONCLUSION
• The image segmentation is done using k-means clustering in 3-D RGB space, so it works perfectly fine with all images.
• The clarity in the segmented image is very good compared to other segmentation techniques.
• The clarity of the image also depends on the number of clusters used.
• One disadvantage of the procedure used is that the number of clusters is to be defined in each iteration.
[attachment=33614]
ABSTRACT:
In this work the way is described an algorithm, which are used to segmentation of images with clustering methods. This algorithm is tested on ten color images, which are firstly transformed to R*B*G* color space. Conditions, results and conclusions are described lower. The results are compared using both Mahalanobis and Euclidean distances in the clustering algorithm.
INTRODUCTION:
Image segmentation was, is and will be a major research topic for many image processing researchers. The reasons are obvious and applications endless: most computer vision and image analysis problems require a segmentation stage in order to detect objects or divide the image into regions which can be considered homogeneous according to a given criterion, such as color, motion, texture, etc.
Clustering is the search for distinct groups in the feature space. It is expected that these groups have different structures and that can be clearly differentiated. The clustering task separates the data into number of partitions, which are volumes in the n-dimensional feature space. These partitions define a hard limit between the different groups and depend on the functions used to model the data distribution.
Image segmentation:
To humans, an image is not just a random collection of pixels; it is a meaningful arrangement of regions and objects. There also exits a variety of images: natural scenes, paintings, etc. Despite the large variations of these images, humans have no problem to interpret them. Considering the large databases on the WWW, in our personal photograph folders, a strong and automatic image analysis would be welcome.
Image Segmentation by Clustering
Clustering is a classification technique. Given a vector of N measurements describing each pixel or group of pixels (i.e., region) in an image, a similarity of the measurement vectors and therefore their clustering in the N-dimensional measurement space implies similarity of the corresponding pixels or pixel groups. Therefore, clustering in measurement space may be an indicator of similarity of image regions, and may be used for segmentation purposes.
The vector of measurements describes some useful image feature and thus is also known as a feature vector. Similarity between image regions or pixels implies clustering (small separation distances) in the feature space. Clustering methods were some of the earliest data segmentation techniques to be developed.
The K-Means Algorithm Process
• The dataset is partitioned into K clusters and the data points are randomly assigned to the clusters resulting in clusters that have roughly the same number of data points.
• For each data point:
• Calculate the distance (Mahalanobis or Euclidean) from the data point to each cluster.
• If the data point is closest to its own cluster, leave it where it is. If the data point is not closest to its own cluster, move it into the closest cluster.
• Repeat the above step until a complete pass through all the data points results in no data point moving from one cluster to another. At this point the clusters are stable and the clustering process ends.
• The choice of initial partition can greatly affect the final clusters that result, in terms of inter-cluster and intra-cluster distances and cohesion.
CONCLUSION
• The image segmentation is done using k-means clustering in 3-D RGB space, so it works perfectly fine with all images.
• The clarity in the segmented image is very good compared to other segmentation techniques.
• The clarity of the image also depends on the number of clusters used.
• One disadvantage of the procedure used is that the number of clusters is to be defined in each iteration.