Indexing and retrieval of 3D models aided by active learning
Indexing and retrieval of 3D models aided by active learning
复制标题
主动学习辅助的 3D 模型索引和检索
DOI：
10.1145/500141.500261
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发表时间：
2001
期刊：
影响因子：
--
通讯作者：
Tsuhan Chen
中科院分区：
文献类型：
--
作者：
Cha Zhang;Tsuhan Chen
我们展示了一个系统的索引和检索的3D模型辅助主动学习。我们提出了一套新的基于区域的3D模型的功能。每个模型都被视为具有均匀密度的实体体积。直接从网格模型有效地计算诸如体积表面比、矩不变量和傅立叶变换系数的特征。与其他功能，如脐带直方图，三维形状谱等，以进一步提高性能，我们将隐藏的注释到我们的系统中实现了相当的检索性能。我们建议使用主动学习来提高标注效率。我们表明，主动学习，系统可以比随机标注更好地执行，和检索结果迅速提高标注样本的数量。此外，在系统中包括相关反馈，并结合主动学习，这提供了更好的用户自适应检索结果。
We demonstrate a system for indexing and retrieval of 3D models aided by active learning. We propose a new set of region-based features for 3D models. Each model is treated as a solid volume with a uniform density. Features such as the volume-surface ratio, the moment invariants and the Fourier transform coefficients are efficiently calculated from the mesh model directly. Comparable retrieval performance is achieved with other features such as the cord histogram, the 3D shape spectrum, etc. To further improve the performance, we incorporate hidden annotation into our system. We propose to use active learning to improve the annotation efficiency. We show that with active learning, the system can perform better than random annotation, and the retrieval result improves rapidly with the number of annotated samples. Moreover, relevance feedback is included in the system and combined with active learning, which provides better user-adoptive retrieval results.