Hashing Cross-Modal Manifold for Scalable Sketch-Based 3D Model Retrieval
Hashing Cross-Modal Manifold for Scalable Sketch-Based 3D Model Retrieval
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DOI：
10.1109/3dv.2014.72
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发表时间：
2014-12
期刊：
影响因子：
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通讯作者：
T. Furuya;Ryutarou Ohbuchi
中科院分区：
文献类型：
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作者：
T. Furuya;Ryutarou Ohbuchi
本文提出了一种新颖的基于草图的3D模型检索算法，该算法具有可扩展性和准确性。准确性是通过以下两个方面的组合来实现的：（1）一组用于比较草图和3D模型的最先进的视觉功能，以及（2）一种有效的算法，用于学习草图和3D模型的异构域之间的数据驱动相似性。对于后者，我们采用了Furuya等人的算法[18]，其为了更精确的相似性计算而融合了三种相似性，即，草图之间、3D模型之间以及草图和3D模型之间的关系。虽然Furuya等人的算法。[18]确实提高了准确性，但它不能扩展。我们通过将其跨模态相似性图嵌入到汉明空间中来加速[18]的检索结果排名阶段，而不损失准确性。嵌入是由频谱嵌入和散列到紧凑的二进制代码的组合。实验结果表明，该算法比以往基于草图的三维模型检索算法具有更高的检索精度和更快的检索速度。
This paper proposes a novel sketch-based 3D model retrieval algorithm that is scalable as well as accurate. Accuracy is achieved by a combination of (1) a set of state-of-the-art visual features for comparing sketches and 3D models, and (2) an efficient algorithm to learn data-driven similarity across heterogeneous domains of sketches and 3D models. For the latter, we adopted the algorithm [18] by Furuya et al., which fuses, for more accurate similarity computation, three kinds of similarities, i.e., Those among sketches, those among 3D models, and those between sketches and 3D models. While the algorithm by Furuya et al. [18] does improve accuracy, it does not scale. We accelerate, without loss of accuracy, retrieval result ranking stage of [18] by embedding its cross-modal similarity graph into Hamming space. The embedding is performed by a combination of spectral embedding and hashing into compact binary codes. Experiments show that our proposed algorithm is more accurate and much faster than previous sketch-based 3D model retrieval algorithms.