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

TL;DR

This paper introduces a computational model for 2D shape decomposition based on the short-cut rule, inspired by human visual preferences for shortest cuts, and demonstrates improved alignment with human intuition over existing methods.

Contribution

The paper presents a novel shape decomposition algorithm that incorporates the short-cut rule, effectively modeling human visual preferences and outperforming prior techniques.

Findings

The proposed method produces decompositions that better match human perception.

It efficiently filters and evaluates cut hypotheses based on local properties and length.

The approach outperforms state-of-the-art methods in psychological experiment comparisons.

Abstract

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.