An unsupervised approach for semantic place annotation of trajectories based on the prior probability

2022-04-20 · Junyi Cheng, Xianfeng Zhang, Peng Luo, Jie Huang, Jianfeng Huang

Semantic place annotation can provide individual semantics, which can be of great help in the field of trajectory data mining. Most existing methods rely on annotated or external data and require retraining following a change of region, thus preventing their large-scale applications. Herein, we propose an unsupervised method denoted as UPAPP for the semantic place annotation of trajectories using spatiotemporal information. The Bayesian Criterion is specifically employed to decompose the spatiotemporal probability of the candidate place into spatial probability, duration probability, and visiting time probability. Spatial information in ROI and POI data is subsequently adopted to calculate the spatial probability. In terms of the temporal probabilities, the Term Frequency Inverse Document Frequency weighting algorithm is used to count the potential visits to different place types in the trajectories, and generates the prior probabilities of the visiting time and duration. The spatiotemporal probability of the candidate place is then combined with the importance of the place category to annotate the visited places. Validation with a trajectory dataset collected by 709 volunteers in Beijing showed that our method achieved an overall and average accuracy of 0.712 and 0.720, respectively, indicating that the visited places can be annotated accurately without any external data.

📄 PDF Abstract BibTeX arXiv:2204.09054

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FG-Depth: Flow-Guided Unsupervised Monocular Depth Estimation

2023-01-20 · Junyu Zhu, Lina Liu, Yong liu, Wanlong Li 외

The great potential of unsupervised monocular depth estimation has been demonstrated by many works due to low annotation cost and impressive accuracy comparable to supervised methods. To further improve the performance, …

Depth Estimation Image Reconstruction Monocular Depth Estimation Semantic Segmentation+1

PointDC:Unsupervised Semantic Segmentation of 3D Point Clouds via Cross-modal Distillation and Super-Voxel Clustering

2023-04-18 · Zisheng Chen, Hongbin Xu, Weitao Chen, Zhipeng Zhou 외

Semantic segmentation of point clouds usually requires exhausting efforts of human annotations, hence it attracts wide attention to the challenging topic of learning from unlabeled or weaker forms of annotations. In this…

Clustering Segmentation Semantic Segmentation Unsupervised Semantic Segmentation

PointDC: Unsupervised Semantic Segmentation of 3D Point Clouds via Cross-Modal Distillation and Super-Voxel Clustering

2023-01-01 · ICCV 2023 1 · Zisheng Chen, Hongbin Xu, Weitao Chen, Zhipeng Zhou 외

Semantic segmentation of point clouds usually requires exhausting efforts of human annotations, hence it attracts wide attention to a challenging topic of learning from unlabeled or weaker form of annotations. In thi…

Clustering Segmentation Semantic Segmentation Unsupervised Semantic Segmentation

SmooSeg: Smoothness Prior for Unsupervised Semantic Segmentation

2023-10-27 · NeurIPS 2023 11 · Mengcheng Lan, Xinjiang Wang, Yiping Ke, Jiaxing Xu 외

Unsupervised semantic segmentation is a challenging task that segments images into semantic groups without manual annotation. Prior works have primarily focused on leveraging prior knowledge of semantic consistency or pr…

Segmentation Self-Supervised Learning Semantic Segmentation Unsupervised Semantic Segmentation

Unsupervised Hierarchical Skill Discovery

2026-01-30 · Damion Harvey, Geraud Nangue Tasse, Benjamin Rosman, Branden Ingram 외 arxiv

We consider the problem of unsupervised skill segmentation and hierarchical structure discovery in reinforcement learning. While recent approaches have sought to segment trajectories into reusable skills or options, most…

Reinforcement Learning