Image segmentation by clustering in the

color space

CIS581 Final Project

Student: Qifang Xu

Advisor: Dr. Longin Jan Latecki

Content Introduction

Project Algorithm

Project program

Experiments and results

Conclusion

Introduction Image segmentation is to find objects or

meaningful part of objects. Two types of segmentation methods

homogeneity

contrast

Image segmentation techniques region growing and shrinking

clustering methods

Boundary detection

Clustering Techniques

Segment an image by grouping each elements based on some measure of similarity

Domain spaces spatial domain (row-column (rc) space)

color space

histogram spaces

other complex feature space

Clustering Algorithms Basic idea

1. Iteratively divide the space of interest into regions by median.

2. stop when the specific criteria is reached.

k-means clustering Recursive region splitting algorithm

standard technique

1. compute histograms for each component of interest (red, green, blue)

2. select a best threshold to split the image into two regions

3. Repeat 1 and 2, until no new regions can be created

Project Algorithm

A combination of the k-medoid algorithm and classification tree’s techniques

Feature space: RGB Computation time is linear to the number of f

eature vectors Flow chart for the project

Project program (1)

Main file: rgbcluster.m

firstauto3(inputFileName, mnp, percent)

inputFileName: image for segmentation

mnp: Minimal number of points in each cluster

percent: parameter that delays clustering of points in a margin region. Value: 0.01-- 0.1. Normal value: 0.05.

output: a set of clusters

Project program (2)

Color indexeach entry: R G B weightsweight is the number of pixels for this color

function [colorWeights] = getStat(data, map) colorWeights = map; [row, col] = size(map); for i = 1:row tmp = find(data==i); colorWeights(i, col+1) = length(tmp); end return;

Project program (3)

Means

meanR =

meanG =

meanB =

N

weightsr *

N

weightsg *

N

weightsb *

Codes for find rgb mean

function [mn] = find_mean(data)[row, col] = size(data);sumR = 0;sumG = 0;sumB = 0;count = 0;for i = 1:row

sumR = sumR + data(i, 1) * data(i, col); sumG = sumG + data(i, 2) * data(i, col); sumB = sumB + data(i, 3) * data(i, col); count = count + data(i, col);

endthisMean(1) = sumR / count;thisMean(2) = sumG / count;thisMean(3) = sumB / count;mn = thisMean;

return;

Project program (4)

Distance: Euclidean distance between two points

Codesfunction [dist] = dist2pt(x, y)

dist = sqrt((x(1)-y(1))^2 + (x(2)-y(2))^2 + (x(3)-y(3))^2);

return;

Project program (5)

Split2.m (provided by Dr. Latecki)

[lmainindex,rmainindex,ldata,ldist,rdata,rdist,cluster,no,centroid] = split2(data,dist,a,mainindex,scale,mnp,percentage,cluster,no,centroid)

Project program (6)

recurauto1.m: recursively split data into clusters (provided by Dr. Latecki)

1. distance histogram (myhist)

2. threshold (evo2)

3. split (split2)

4. find left distance, go to left branch

5. find right distance, go to right branch

6. no new split, stop

Unclustered points

assigned to the clusters with closest distance to the centroids.

Experiments and results

Rgb pictures Cherry flowers

House

tiger

airplane

car

people

Resluts 1

Mnp: 30, percent 0.05, cluster number 4

Mnp : 20, percent 0.05, cluster number 7

Original pictures segmented pictures

Results 2

Mnp: 10, percent 0.05, cluster number: 9

Mnp: 50, percent 0.05, cluster number: 3

Original pictures Segmented pictures

Results 3Original pictures segmented pictures

Mnp: 10, percent: 0.05, cluster number: 11

Mnp: 30

Percent: 0.05

Cluster number: 4

Image size: 2MB, mnp: 30, cluster number: 5

Mnp: 30, Cluster number: 4

Mnp:10, cluster number: 15Original picture

Results movies

Conclusion

Advantages no predefined cluster number user interactive computation time

Disadvantages spatial information lost Cannot deal with noise or outliers