Discrete curve model for non-elastic shape analysis on shape manifold. (October 2022)

• Record Type: Journal Article
• Authors: Chen, Peng; Li, Xutao; Ding, Changxing; Liu, Jianxing; Wu, Ligang
• Is Part Of: Pattern recognition. Volume 130(2022)
• Publication Date: 2022-10
• Subjects: Discrete curve model -- Non-elastic shape analysis -- Shape manifold -- Shape synthesis -- Shape retrieval -- Shape arithmetics; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2022.108760
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: We construct a shape manifold and its tangent space for the discrete curve model. We design a geodesic distance that significantly improves the retrieval score. Our method overcomes some limitations in bending-only model. We propose a novel application named shape arithmetic. Abstract: In this paper, we construct a novel finite dimensional shape manifold for shape analyses. Elements of the shape manifold are a set of discrete, planar, and closed curves, which stand for object boundaries and are represented by direction function. On this manifold, we use a set of N-dimensional Fourier basis to construct the tangent space of the shape manifold as a finite dimensional space. Furthermore, we construct the shape manifold as a Riemannian manifold, in which the Riemannian metric is interpreted as an l 2 metric. Our method improves the performance of bending-only models in the issues of shape analysis including the shape synthesis, comparison, and statistic analysis. We evaluate the performance of the manifold via the following applications: 1) shape interpolation and extrapolation between curves, 2) shape retrieval on the Flavia leaf database, 3) shape synthesis using an estimated probability distribution on the manifold, and 4) a novel application named shape arithmetic. All the above experiments clearly demonstrate our approach achieves superior performance to state-of-the-art methods.