7/29/2019 Fuzzy 21st

1/2

FUZZY C-MEANS CLUSTERING

Introduction:

Image segmentation, the partitioning of an image into homogeneous regions based on a set of

characteristics, is a key element in image analysis and computer vision. Clustering is one of themethods available for this purpose. Clustering is a process which can be used for classifying pixels

based on similarity according to the pixel's color or gray-level intensity.

The K-means algorithm has been used for a fast and crisp "hard" segmentation. The Fuzzy Set theory

has improved this process by allowing the concept of partial membership, in which an image pixel can

belong to multiple clusters. This "soft" clustering allows for a more precise computation of the cluster

membership, and has been used successfully for image clustering and for the unsupervised

segmentation of medical, geological, and satellite images.

Module:

Image Selection

Applying C-Means Clustering

Module Description:

Image Selection:

Fuzzy image processing is not a unique theory. It is a collection of different fuzzy approaches to

image processing

In this module the user need to select a image from a system,

the selected image will appear in application, now the select image ready to go for clustering...

Fuzzification and Defuzzification :

Fuzzy image processing is the collection of all approaches that understand, represent and process the

images, their segments and features as fuzzy sets.

The fuzzification and defuzzification steps are due to the fact that we do not possess fuzzy hardware.

Therefore, the coding of image data (fuzzification) and decoding of the results (defuzzification) aresteps that make possible to process images with fuzzy techniques. The main power of fuzzy image

processing is in the middle step (modification of membership values, see Fig.2). After the image data

are transforemd from gray-level plane to the membership plane (fuzzification), appropriate fuzzy

techniques modify the membership values. This can be a fuzzy clustering, a fuzzy rule-based

approach, a fuzzy integration approach and so on.

7/29/2019 Fuzzy 21st

2/2

SYSTEM SPECIFICATION

HARDWARE SPECIFICATION:

PROCESSOR : PENTIUM CELERON 733MHZ

BUILT IN MEMORY : 512 MB RAM

HARD DISK CAPACITY : 40GB

MONITOR : SAMSUNG

KEYBOARD : SAMSUNG

MOUSE : LOGITECH

SOFTWARE SPECIFICATION:

OPERATING SYSTEM : WINDOWS XP AND ABOVE

FRONT-END : VB.NET 2008

Existing:

Difficulty in comparing quality of the clusters produced (e.g. for different initial partitions or valuesof K affect outcome).Fixed number of clusters can make it difficult to predict what K should be.Does

not work well with non-globular clusters.Different initial partitions can result in different final

clusters. It is helpful to rerun the program using the same as well as different K values, to compare the

results achieved.

Proposed :

Algorithm :

The fuzzy C-means (FCM) algorithm follows the same principles as the K-means algorithm in that it

compares the RGB value of every pixel with the value of the cluster center. The main difference isthat instead of making a hard decision about which cluster the pixel should belong to, it assigns a

value between 0 and 1 describing "how much this pixel belongs to that cluster" for each cluster. Fuzzy

rule states that the sum of the membership value of a pixel to all clusters must be 1. The higher the

membership value, the more likely that pixel is to belong to that cluster. The FCM clustering is

obtained by minimizing an objective function .