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Sparse representation-based 3D model retrieval

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Abstract

In this study, we leveraged the sparse representation for multi-modal information fusion to handle 3D model retrieval problem. First, SIFT feature is extracted to represent the visual appearance of 2D view images for each 3D models. With this low-level feature representation, the Latent Dirichlet Allocation model is learned to generate the high-level & discriminative visual representation for individual 3D model. Then, we utilize the sparse representation framework to handle the key problem, the similarity measure between two different 3D models, for model retrieval. The performance of the proposed method is evaluated on the novel MV-RED 3D object dataset, which contains both RGB and depth 3D model data. The comparison experiments demonstrate the proposed sparse representation-based framework can benefit from multi-modal information fusion and consequently augment the performance.

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China (61502337, 61472275, 61170239, 61303208), the Tianjin Research Program of Application Foundation and Advanced Technology (15JCYBJC162000), and the grant of Elite Scholar Program of Tianjin University (2014XRG-0046).

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  1. School of Electronics Information Engineering, Tianjin University, Tianjin, China

    Qun Cao, Yang An, Yingdi Shi & Xiaorong Zhu

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  1. Qun Cao
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  2. Yang An
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  3. Yingdi Shi
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  4. Xiaorong Zhu
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Correspondence to Yang An or Xiaorong Zhu.

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Cao, Q., An, Y., Shi, Y. et al. Sparse representation-based 3D model retrieval. Multimed Tools Appl 76, 20069–20079 (2017). https://doi.org/10.1007/s11042-016-4238-9

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