Imgcluster

Image clustering using the similarity algorithms: SIFT, SSIM, CW-SSIM, MSE

This project aims to implement the clustering of images by utilizing Spectral Clustering and Affinity Propagation Clustering together with a number of similarity algorithms, like:

• SIFT: Scale-invariant Feature Transform
• SSIM: Structural Similarity Index
• CW-SSIM: Complex Wavelet Structural Similarity Index
• MSE: Mean Squared Error

The best clustering results are selected according to the calculated performance metrics for clustering:

• Silhouette Coefficient
• Completeness Score
• Homogeneity Score

IPython Notebook (.ipynb file) is included for step-by-step execution of the demo application with extra comments.

The project is using OpenCV 3.1, Scikit-Learn, Scikit-Image and PySSIM for image manipulations, similarity measurements and clustering.

All images have been downloaded from the free-of-charge online service Pixabay: https://pixabay.com

Any kind of copyrights, ownership rights or distribution rights have been considered according to the information, which is available on Pixabay.

Please feel free to ask any questions: [email protected]