A two-stage shape retrieval (TSR) method with global and local features

• A two-stage shape retrieval (TSR) method to address the 2D shape retrieval problem is proposed.
• The cause of unreliable retrieval results in all state-of-the-art methods is identified.
• TSR decomposes the decision process into two stages: ICF and LMR.
• TSR removes irrelevant shapes by considering local and global features simultaneously.
• TSR outperforms all other methods on three popular datasets: MPEG-7, Kimia99 and Tari1000.

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.