Image-Segmentation

• Image segmentation means that we can group similar pixels together and give these grouped pixels the same label. The grouping problem is a clustering problem. We used K-means and spectral clustering on the Berkeley Segmentation Benchmark.
• We will talk about each technique and the results of the evaluation using F-measures and Conditional Entropy.

It's a university project for Pattern Recognition at Faculty of Engineering, Alexandria University. Computer and Communication Program.

FIND COLAB LINK FOR THE PROJECT.

https://colab.research.google.com/drive/13EsG-s_NrtyYGUhKPM45AKwPfEHGYze5?usp=sharing

Before running!

1. Download BSR.zip, which includes the 50 images with their truth ground images.

Steps

• Below we will show the needed steps to achieve the goal of the assignment.

1. Download the Dataset.

• The dataset contains two folders of images, the original images and the ground truth images which are saved in matlab file format. Each image has 5 ground truth segmentation that will be used later for evaluations. We worked on 50 images with different categories and sizes.

2. Visualize the image and the ground truth segmentation.

• display an image with its associated ground truth segmentation.
• display an image with its associated ground truth contour segmentation.

3. Segmentation using K-means

• Every image pixel is a feature vector of 3-dimension {R, G, B}.

Steps:

• A. We will change the K of the K-means algorithm between {3,5,7,9,11} clusters. that will produce different segmentations and will save them as colored images. Every color represents a certain group (cluster) of pixels.
• B. We will evaluate the result segmentation using F-measure, Conditional Entropy for image I with M available ground-truth segmentations.
• C. Display good results and bad results for every configuration in A, B.

Sample output

 ** Average fmeasure for each K **
[0.65364406 0.52427345 0.48359772 0.42858945 0.39952321]
 ** Average entropy for each K **
[0.40142372 0.34338245 0.32296092 0.31982477 0.28262732]

 ** Average fmeasure for each K **
[0.42675117 0.37018466 0.37081968 0.33621452 0.32035279]
 ** Average entropy for each K **
[0.82823465 0.78931538 0.72647158 0.69597183 0.68292893]

4. Big Picture

• In this section we will compare between ground truth images and the result of k-means and the spectral clustering techniques.

A. K-means vs Ground Truth

• Select a set of five images and display their corresponding ground truth against your segmentation results using K-means at K=5.

Segmentation results for random image using K-means at k = 5.

Image corresponding ground truth.

B. Spectral Clustering vs Real image.

Real image

Segmentation results for random image using Normalized-cut for the 5- NN graph, at k = 5.

A - With 10% of it's orginal size. *** B - With 15% of it's orginal size

C. Spectral Clustering vs K-means vs Real image.

• Select five images and contrast your segmentation results using Normalized-cut for the 5-NN graph, at K=5 versus using K- means at K=5.

Random Real image

Spectral Clustering vs K-means

5. Extra

• In the previous parts, we used the color features RGB. We did not encode the layout of the pixels. We want to modify that for K-means clustering to encode the spatial layout of the pixels.
  • i. Modify the feature vector to include spatial layout.
  • ii. Contrast the results you obtained in 4.A to the results you obtained by considering the spatial layout.

*** FOR IMAGE NB: 1 ***

 ** Average fmeasure for each K **
[0.82205757 0.58572982 0.4798646  0.40293263 0.33990403]
 ** Average entropy for each K **
[0.19845532 0.1867943  0.17868015 0.174505   0.17282233]

*** FOR IMAGE NB: 2 ***

 ** Average fmeasure for each K **
[0.77222293 0.68747015 0.53652423 0.4558225  0.38230512]
 ** Average entropy for each K **
[0.4075518  0.35545733 0.30010906 0.29462604 0.28558238]

GROUP TEAM

• Mahmoud Elkarargy
• Hossam Elkady
• Mariam Youssef
• Raneem Ahmed