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 feature 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: Normal value: output: a set of clusters

Project program (2)  Color index each entry: R G B weights weight 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 =

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

Project program (4)  Distance: Euclidean distance between two points  Codes function [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 3 Original 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: 15 Original picture

Results movies

Conclusion  Advantages  no predefined cluster number  user interactive  computation time  Disadvantages  spatial information lost  Cannot deal with noise or outliers