This repository contains my implementation of the GrabCut Algorithm [1], a markov random field based image segmentation algorithm, which is based on is based on multiple iterations of GraphCut [2], and has a provision for humans to intervene to correct the model's predicted segmentation.
In the experiments, I manipulate the following parameters and report change in performance across the running times and accuracy:
- Number of iterations
Gammawhich is used to model the penalty if two neighboring nodes had different labels- Number of GMMs allotted per label
- Effect of the tightness of the initial bounding box
- Neighborhood being defined as 4-based connectivity or 8-based connectivity
The details of my implementation can be found in 01 Report Implementation.pdf.
The details of experimentation can be found in 02 Report Experiments.pdf.
A consolidated report is present at: FINAL_REPORT.pdf
Assignment3.ipynb: Has the content corresponding to deliverable 1 (the good and bad examples and ALSO the examples which included user refinement heatmaps)src/Experiments_<parameter name>.ipynb: have the images/results used in the experiments which have been used to derive the inferences present in the REPORT
ans_data.json: contains the scores for all images across 10 iterations (with default parameters) and was used to make the table in deliverable 1- DIRECTORY
ans_images: contains directories for each image and stores snapshots of the GrabCut pipeline on each image across iterations
[1] Carsten Rother, Vladimir Kolmogorov, and Andrew Blake. 2004. "GrabCut": interactive foreground extraction using iterated graph cuts. ACM Trans. Graph. 23, 3 (August 2004), 309–314. DOI:https://doi.org/10.1145/1015706.1015720
[2] Y. Y. Boykov and M. -. Jolly, "Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images," Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, 2001, pp. 105-112 vol.1, doi: 10.1109/ICCV.2001.937505.