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ModelSeek: an effective 3D model retrieval system

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Abstract

Since object similarity is a subjective matter, the gap between low-level feature representations and high-level semantic concepts is a major problem in the field of content-based 3D model retrieval. This paper presents a novel composite model descriptor, which takes into account both visual and geometric characteristics of 3D models. It also proposes an original mapping mechanism from low-level model features to high-level semantic concepts based on the user’s retrieval history, and so this method belongs to long-term relevance feedback algorithms for 3D model retrieval. Finally, an effective 3D model retrieval system “ModelSeek” has been built, and implemented with the introduced model descriptor and mapping mechanism. The experimental results show that the approaches above not only have significantly improved the retrieval performance, but have also achieved better retrieval effectiveness than the state-of-the-art techniques on the publicly available 3D model criterion that Princeton Shape Benchmark (PSB) and several standard evaluation measures.

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Acknowledgements

The 3D model database PSB is from the Shape Retrieval and Analysis Group at the University of Princeton, United States. This work is supported by the Key Projects in the National Science & Technology Pillar Program during the Eleventh Five-Year Plan Period (No. 2006BAB04A13), and the National Natural Science Foundation of China (No. 60803120).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Beihang University, Beijing, People’s Republic of China

    Biao Leng & Zhang Xiong

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  1. Biao Leng
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  2. Zhang Xiong
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Correspondence to Biao Leng.

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Leng, B., Xiong, Z. ModelSeek: an effective 3D model retrieval system. Multimed Tools Appl 51, 935–962 (2011). https://doi.org/10.1007/s11042-009-0424-3

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