A study and comparison of different image segmentation algorithms

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April 5, 2017
A Study and Comparison of Different Image
Segmentation Algorithms

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Preamble
 Project Title : Detection of counterfeit Indian currency note
 Seminar Title : A Study and Comparison of Different Image
Segmentation Algorithms
 In our project we are dividing an image into 3x3 grid and
extract the required features and compare it with the
database. The paper has given vivid description about the
different segmentation algorithms, which are used in the
applications like pattern recognition and image analysis.

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Agenda
 Introduction
 Image segmentation techniques
 Results and discussion
 Conclusion
 References

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Section-1 : INTRODUCTION

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Continued..

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Section-2:Image Segmentation Techniques

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Thresholding base Image Segmentation
 Image segmentation based on a thresholding is the
simplest technique
 In this technique we set a threshold value (mostly from
the histogram of the image)
 Pixel lying above or below can be classify as object and
background
 This technique convert a gray scale image into binary
image
 This technique will give good result if background and
object has large variation in their intensity value
 The disadvantage is that it will not be able to identify
multiple object.

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Image segmentation algorithms can be classified
into two classes:
• Global segmentation algorithms
• Local segmentation algorithms
Algorithms:

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Region-base Image Segmentation
Region based segmentation can be done in two ways:
 Region Growing
 Data Clustering
1. Region Growing: Region growing is simplest in
region base image segmentation techniques. In this
technique, a seed point is chosen at random, then
neighboring pixels are check, with some criterion, to
determine whether those neighboring pixels are
added to the initial seed points or not.

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2. Data clustering: Data clustering method initially
assume whole image as a single cluster and then use
mathematics and statistics to create number of clusters
within the image.
Two types of clustering are possible:
 Hierarchical clustering
 Partitional clustering
 In the hierarchical clustering, we can
change the numbers of cluster during the
process.
 In the partitional clustering, we must decide
the numbers of cluster before processing.
Continued…

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 Edge based segmentation techniques are first find the edges by
using different-different operators.
 Since an object can be represented by its edges. So we can
segment the image by simply finding edges in the image. A
typical approach to segmentation using edges is:
• compute an edge image from original image
• process the edge image for broken edges
• transform the result to an ordinary segmented
image by filling in the object boundaries
 watershed segmentation technique is one more technique
which can be used to process the edge image.
Edge base Image Segmentation

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Section-3: Results and Comparison
Original Images

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Result obtained by Otsu's Algorithm

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Result obtained by K-Mean Clustering technique

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Result obtained by quad tree technique

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Result obtained by delta E technique

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Result obtained by FTH technique

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 Used and compared the performance 6 image segmentation algorithms.
 We have apply these algorithm on a very simple image (bear) to a very
complex image (man woman).
 From the simulation results, we can conclude that if test image is simple
(one object) than Delta-E perform better as compare to other algorithm.
 Although Otsu's and Kmean algorithm perform similar to Delta-E but they
falsely consider background as a object.
 As the Complexity of input test image increases, for single object
segmentation, performance degraded. As we can see from tiger food image.
for Complex image, if we run the same code for number of objects, than we
might get the good performance and this would be a future work.
Conclusion

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Questions??
Stay Hungry Stay Foolish
“Steve Jobs”

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