A Method of Perceptual-Based Shape Decomposition

Chang Ma, Zhongqian Dong, Tingting Jiang, Yizhou Wang, Wen Gao; The IEEE International Conference on Computer Vision (ICCV), 2013, pp. 873-880


In this paper, we propose a novel perception-based shape decomposition method which aims to decompose a shape into semantically meaningful parts. In addition to three popular perception rules (the Minima rule, the Short-cut rule and the Convexity rule) in shape decomposition, we propose a new rule named part-similarity rule to encourage consistent partition of similar parts. The problem is formulated as a quadratically constrained quadratic program (QCQP) problem and is solved by a trust-region method. Experiment results on MPEG-7 dataset show that we can get a more consistent shape decomposition with human perception compared with other state-of-the-art methods both qualitatively and quantitatively. Finally, we show the advantage of semantic parts over non-meaningful parts in object detection on the ETHZ dataset.

Related Material

author = {Ma, Chang and Dong, Zhongqian and Jiang, Tingting and Wang, Yizhou and Gao, Wen},
title = {A Method of Perceptual-Based Shape Decomposition},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {December},
year = {2013}