# A Computational Model of the Short-Cut Rule for 2D Shape Decomposition

arXiv: https://arxiv.org/abs/1409.2104 · HTML: https://mlsanity.com/paper/1409.2104/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Sep 2014.

## Built on by (most cited first, 1 of 1)

- [Image Collage on Arbitrary Shape via Shape-Aware Slicing and Optimization](https://mlsanity.com/paper/2401.02435/) (2023)

## Abstract (arXiv)

We propose a new 2D shape decomposition method based on the short-cut rule. The short-cut rule originates from cognition research, and states that the human visual system prefers to partition an object into parts using the shortest possible cuts. We propose and implement a computational model for the short-cut rule and apply it to the problem of shape decomposition. The model we proposed generates a set of cut hypotheses passing through the points on the silhouette which represent the negative minima of curvature. We then show that most part-cut hypotheses can be eliminated by analysis of local properties of each. Finally, the remaining hypotheses are evaluated in ascending length order, which guarantees that of any pair of conflicting cuts only the shortest will be accepted. We demonstrate that, compared with state-of-the-art shape decomposition methods, the proposed approach achieves decomposition results which better correspond to human intuition as revealed in psychological experiments.