Embedding Retrieval of Articulated Geometry Models
Embedding Retrieval of Articulated Geometry Models
复制标题
铰接几何模型的嵌入检索
DOI：
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复制
发表时间：
2012
影响因子：
23.6
通讯作者：
Rynson W. H. Lau
中科院分区：
文献类型：
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作者：
G. Tam;Rynson W. H. Lau
由于计算机游戏和动画的普及，三维关节几何模型检索的研究近年来引起了人们的极大关注。然而，现有的大多数工作都提取高维特征来表示模型，并受到实际应用的限制。首先，高维特征的不对准可能会产生不可靠的欧几里德距离，并影响检索精度。其次，维度的诅咒也降低了效率。在本文中，我们提出了一个嵌入式检索框架，以提高这些方法的实用性。它基于一种流形学习技术--扩散映射(DM)。我们将所有成对距离投影到一个低维空间。这提高了检索精度，因为簇间距离被夸大了。在此基础上，采用密度加权Nyström扩展，并进一步提出了一种新的方法，将Nyström嵌入与特征分解嵌入进行局部对齐，以减少扩展误差并保持检索精度。最后，我们提出了一种处理不连通流形的启发式算法，通过增加多个相似度量和最短边来扩充核矩阵，并进一步讨论了DM参数的选择。我们已经结合了两个现有的匹配算法进行测试。我们的实验结果表明，在高召回率和速度方面，准确率都有所提高。我们的工作为流形上的多媒体数据匹配提供了一个健壮的检索框架。
Due to the popularity of computer games and animation, research on 3D articulated geometry model retrieval has attracted a lot of attention in recent years. However, most existing works extract high-dimensional features to represent models and suffer from practical limitations. First, misalignment in high-dimensional features may produce unreliable euclidean distances and affect retrieval accuracy. Second, the curse of dimensionality also degrades efficiency. In this paper, we propose an embedding retrieval framework to improve the practicability of these methods. It is based on a manifold learning technique, the Diffusion Map (DM). We project all pairwise distances onto a low-dimensional space. This improves retrieval accuracy because intercluster distances are exaggerated. Then we adapt the Density-Weighted Nyström extension and further propose a novel step to locally align the Nyström embedding to the eigensolver embedding so as to reduce extension error and preserve retrieval accuracy. Finally, we propose a heuristic to handle disconnected manifolds by augmenting the kernel matrix with multiple similarity measures and shortcut edges, and further discuss the choice of DM parameters. We have incorporated two existing matching algorithms for testing. Our experimental results show improvement in precision at high recalls and in speed. Our work provides a robust retrieval framework for the matching of multimedia data that lie on manifolds.