A Two-Stage Shape Retrieval (TSR) Method with Global and Local Features
This addresses shape retrieval robustness for computer vision applications, but it appears incremental as it builds on existing local-feature methods.
The paper tackled the 2D shape retrieval problem by proposing a two-stage method that combines global and local features to filter irrelevant shapes before matching, resulting in outperforming existing methods on MPEG-7, Kimia99, and Tari1000 datasets.
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.