southflame/image-segm

Super Pixel Clustering for Image Segmentation

Superpixel Hierarchical Clustering algorithm (SPHC) For Image Segmentation

The idea to create the algorithm came from difficulties in locating online resources describing image segmentation algorithms that use superpixels as a starting point. The conception of the algorithm further came from the observation that neighboring superpixels often have similarities in color and object boundaries are defined by color differences/similarities.

The algorithm takes two main inputs: a RGB pixel grid to represent an image and a grid of segments from the sklearn SLIC superpixel-creating algorithm.

After segment assignment happens, the superpixel hierarchical clustering takes place as follows

1. Loop through these four steps while the smallest cluster euclidean distance is below a specified threshold: 1a. Find neighboring segment pairs for each of the 1 through K superpixels. 1b. Get average R, G, and B values for each segment 1c. For each pair of neighboring segments, calculate euclidean distance using average R, G, and B values. 1d. Merge the 2 segments with the shortest RGB euclidean distance.
2. Output image.

##References

1. Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aure-lien Lucchi, Pascal Fua, and Sabine Susstrunk, SLIC Superpixels, EPFL Technical Report 149300, June 2010.
2. Adrian Rosebrock, A SLIC Superpixel Tutorial using Python, http://www.pyimagesearch.com/2014/07/28/a-slic-superpixel-tutorial-using-python, July 28, 2014.