A two-stage shape retrieval (TSR) method with global and local features |
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| Affiliation: | 1. College of Information Science and Technology, Beijing Normal University, Beijing, China;2. State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China;3. Banner Alzheimer’s Institute and Banner Good Samaritan PET Center, Phoenix, AZ, USA;1. Department of Electrical and Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran;2. Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran;1. School of Science, Tianjin University, 300072, China;2. State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, 300072, China;1. Beijing Key Laboratory of Digital Media, School of Computer Science and Engineering, Beihang University, Beijing 100191, China;2. State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing 100191, China;1. School of Software Engineering, Tongji University, Shanghai 201804, China;2. Nanyang Technological University, Singapore |
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| 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. |
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| Keywords: | 2D shape retrieval Shape representation MPEG-7 shape dataset Kimia99 dataset Tari1000 dataset |
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