Shape Classification Based on Skeleton-Branch Distances

Abstract

In recent decades, the need for efficient and effective image search from large databases has increased. In this paper, we present a novel shape matching framework based on structures that are likely to exist in similar shapes. After representing shapes as medial axis graphs, where vertices show skeletons and edges connect nearby skeletons, we determine the branches connecting or representing shape's different parts. Using the shortest path distance from each vertex (skeleton) to each of the branches, we effectively retrieve similar shapes to the given query through a transportation-based distance function. A set of shape retrieval experiments including the comparison with two previous approaches demonstrate the proposed algorithm's effectiveness and perturbation experiments present its robustness.