Local-HDP：实时的机器人场景中的交互式开放式三维物体分类
Local-HDP: Interactive Open-Ended 3D Object Categorization in Real-Time Robotic Scenarios
摘要：
我们介绍了一个非参数的分层贝叶斯方法的开放式3D对象分类，命名为本地分层狄利克雷过程（本地HDP）。该方法允许智能体增量地学习每个类别的独立主题，并及时适应环境。分层贝叶斯方法，如潜在狄利克雷分配（LDA）可以将低级别的功能转换为高级别的概念主题，用于3D对象分类。然而，基于LDA的方法的效率和准确性取决于手动选择的主题的数量。此外，固定所有类别的主题数量可能会导致模型的过度拟合或欠拟合。相比之下，建议的本地HDP可以自主地确定每个类别的主题的数量。此外，在线变分推理方法已被改编为快速后验近似的局部HDP模型。实验表明，所提出的本地HDP方法优于其他国家的最先进的方法，在准确性，可扩展性和内存效率的大幅度提高。此外，两个机器人实验已经进行了实时的【形】应用程序中所提出的方法的适用性。
英文摘要：
We introduce a non-parametric hierarchical Bayesian approach for open-ended 3D object categorization, named the Local Hierarchical Dirichlet Process (Local-HDP). This method allows an agent to learn independent topics for each category incrementally and to adapt to the environment in time. Hierarchical Bayesian approaches like Latent Dirichlet Allocation (LDA) can transform low-level features to high-level conceptual topics for 3D object categorization. However, the efficiency and accuracy of LDA-based approaches depend on the number of topics that is chosen manually. Moreover, fixing the number of topics for all categories can lead to overfitting or underfitting of the model. In contrast, the proposed Local-HDP can autonomously determine the number of topics for each category. Furthermore, the online variational inference method has been adapted for fast posterior approximation in the Local-HDP model. Experiments show that the proposed Local-HDP method outperforms other state-of-the-art approaches in terms of accuracy, scalability, and memory efficiency by a large margin. Moreover, two robotic experiments have been conducted to show the applicability of the proposed approach in real-time applications.
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