An optimized shape descriptor based on structural properties of networks

TL;DR

This paper introduces an optimized shape descriptor based on network topology analysis of shape boundaries, improving classification accuracy and computational efficiency by leveraging structural network measurements.

Contribution

It presents a novel shape descriptor that utilizes network structural properties, enhancing shape classification and reducing computational complexity.

Findings

Network measurements can distinguish object classes.

Structural measurements improve descriptor discriminant power.

The approach is computationally more efficient.

Abstract

The structural analysis of shape boundaries leads to the characterization of objects as well as to the understanding of shape properties. The literature on graphs and networks have contributed to the structural characterization of shapes with different theoretical approaches. We performed a study on the relationship between the shape architecture and the network topology constructed over the shape boundary. For that, we used a method for network modeling proposed in 2009. Firstly, together with curvature analysis, we evaluated the proposed approach for regular polygons. This way, it was possible to investigate how the network measurements vary according to some specific shape properties. Secondly, we evaluated the performance of the proposed shape descriptor in classification tasks for three datasets, accounting for both real-world and synthetic shapes. We demonstrated that not only degree related measurements are capable of distinguishing classes of objects. Yet, when using measurements that account for distinct properties of the network structure, the construction of the shape descriptor becomes more computationally efficient. Given the fact the network is dynamically constructed, the number of iterations can be reduced. The proposed approach accounts for a more robust set of structural measurements, that improved the discriminant power of the shape descriptors.