A Two-Stage Shape Retrieval (TSR) Method with Global and Local Features

TL;DR

This paper introduces a two-stage shape retrieval method combining global and local features to improve robustness and accuracy in 2D shape retrieval tasks, outperforming existing methods on multiple datasets.

Contribution

The paper proposes a novel two-stage shape retrieval approach that effectively filters irrelevant shapes before local feature matching, enhancing retrieval robustness.

Findings

Outperforms existing shape retrieval methods on MPEG-7, Kimia99, and Tari1000 datasets.

Demonstrates improved robustness and accuracy in 2D shape retrieval.

Effective combination of global and local features in a two-stage process.

Abstract

A robust two-stage shape retrieval (TSR) method is proposed to address the 2D shape retrieval problem. Most state-of-the-art shape retrieval methods are based on local features matching and ranking. Their retrieval performance is not robust since they may retrieve globally dissimilar shapes in high ranks. To overcome this challenge, we decompose the decision process into two stages. In the first irrelevant cluster filtering (ICF) stage, we consider both global and local features and use them to predict the relevance of gallery shapes with respect to the query. Irrelevant shapes are removed from the candidate shape set. After that, a local-features-based matching and ranking (LMR) method follows in the second stage. We apply the proposed TSR system to MPEG-7, Kimia99 and Tari1000 three datasets and show that it outperforms all other existing methods. The robust retrieval performance of the TSR system is demonstrated.