Toward Perception-Based Shape Decomposition

The aim of this work is to decompose shapes into parts which are consistent to human perception. We propose a novel shape decomposition method which utilizes the three perception rules suggested by psychological study: the Minima rule, the Short-cut rule and the Convexity rule. Unlike the previous work, we focus on improving the convexity of the decomposed parts while minimizing the cut length as much as possible. The problem is formulated as a combinatorial optimization problem and solved by a quadratic programming method. In addition, we consider the curved branches which introduce “false” concavity. To solve this problem, we straighten the curved branches before shape decomposition which makes the results more consistent with human perception. We test our approach on the MPEG-7 shape dataset, and the comparison results to previous work show that the proposed method can improve the part convexity while keeping the cuts short, and the decomposition is more consistent with human perception.