TISIS : Trajectory Indexing for SImilarity Search

• URL: http://arxiv.org/abs/2409.11301v2
• Date: Fri, 20 Sep 2024 08:48:30 GMT
• Title: TISIS : Trajectory Indexing for SImilarity Search
• Authors: Sara Jarrad, Hubert Naacke, Stephane Gancarski,
• Abstract summary: Social media platforms enable users to share diverse types of information, including geolocation data. geolocation data can be leveraged to reconstruct the trajectory of a user's visited Points of Interest (POIs) Key requirement is the ability to measure the similarity between such trajectories.
• Score: 0.7373617024876725
• License:
• Abstract: Social media platforms enable users to share diverse types of information, including geolocation data that captures their movement patterns. Such geolocation data can be leveraged to reconstruct the trajectory of a user's visited Points of Interest (POIs). A key requirement in numerous applications is the ability to measure the similarity between such trajectories, as this facilitates the retrieval of trajectories that are similar to a given reference trajectory. This is the main focus of our work. Existing methods predominantly rely on applying a similarity function to each candidate trajectory to identify those that are sufficiently similar. However, this approach becomes computationally expensive when dealing with large-scale datasets. To mitigate this challenge, we propose TISIS, an efficient method that uses trajectory indexing to quickly find similar trajectories that share common POIs in the same order. Furthermore, to account for scenarios where POIs in trajectories may not exactly match but are contextually similar, we introduce TISIS*, a variant of TISIS that incorporates POI embeddings. This extension allows for more comprehensive retrieval of similar trajectories by considering semantic similarities between POIs, beyond mere exact matches. Extensive experimental evaluations demonstrate that the proposed approach significantly outperforms a baseline method based on the well-known Longest Common SubSequence (LCSS) algorithm, yielding substantial performance improvements across various real-world datasets.

Related papers

• T-JEPA: A Joint-Embedding Predictive Architecture for Trajectory Similarity Computation [6.844357745770191]
  Trajectory similarity computation is an essential technique for analyzing moving patterns of spatial data across various applications. We propose T-JEPA, a self-supervised trajectory similarity method employing Joint-Embedding Predictive Architecture (JEPA) to enhance trajectory representation learning.
  arXiv  Detail & Related papers  (2024-06-13T09:51:51Z)
• Cluster-Aware Similarity Diffusion for Instance Retrieval [64.40171728912702]
  Diffusion-based re-ranking is a common method used for retrieving instances by performing similarity propagation in a nearest neighbor graph. We propose a novel Cluster-Aware Similarity (CAS) diffusion for instance retrieval.
  arXiv  Detail & Related papers  (2024-06-04T14:19:50Z)
• COS-Mix: Cosine Similarity and Distance Fusion for Improved Information Retrieval [0.0]
  This study proposes a novel hybrid retrieval strategy for Retrieval-Augmented Generation (RAG) Traditional cosine similarity measure is widely used to capture the similarity between vectors in high-dimensional spaces. We incorporate cosine distance measures to provide a complementary perspective by quantifying the dissimilarity between vectors.
  arXiv  Detail & Related papers  (2024-06-02T06:48:43Z)
• MGeo: Multi-Modal Geographic Pre-Training Method [49.78466122982627]
  We propose a novel query-POI matching method Multi-modal Geographic language model (MGeo) MGeo represents GC as a new modality and is able to fully extract multi-modal correlations for accurate query-POI matching. Our proposed multi-modal pre-training method can significantly improve the query-POI matching capability of generic PTMs.
  arXiv  Detail & Related papers  (2023-01-11T03:05:12Z)
• Contrastive Trajectory Similarity Learning with Dual-Feature Attention [24.445998309807965]
  Tray similarity measures act as query predicates in trajectory databases. We propose a contrastive learning-based trajectory modelling method named TrajCL. TrajCL is consistently and significantly more accurate and faster than the state-of-the-art trajectory similarity measures.
  arXiv  Detail & Related papers  (2022-10-11T05:25:14Z)
• Recall@k Surrogate Loss with Large Batches and Similarity Mixup [62.67458021725227]
  Direct optimization, by gradient descent, of an evaluation metric is not possible when it is non-differentiable. In this work, a differentiable surrogate loss for the recall is proposed. The proposed method achieves state-of-the-art results in several image retrieval benchmarks.
  arXiv  Detail & Related papers  (2021-08-25T11:09:11Z)
• A Taxonomy of Similarity Metrics for Markov Decision Processes [62.997667081978825]
  In recent years, transfer learning has succeeded in making Reinforcement Learning (RL) algorithms more efficient. In this paper, we propose a categorization of these metrics and analyze the definitions of similarity proposed so far.
  arXiv  Detail & Related papers  (2021-03-08T12:36:42Z)
• Centralized Information Interaction for Salient Object Detection [68.8587064889475]
  The U-shape structure has shown its advantage in salient object detection for efficiently combining multi-scale features. This paper shows that by centralizing these connections, we can achieve the cross-scale information interaction among them. Our approach can cooperate with various existing U-shape-based salient object detection methods by substituting the connections between the bottom-up and top-down pathways.
  arXiv  Detail & Related papers  (2020-12-21T12:42:06Z)
• Making Affine Correspondences Work in Camera Geometry Computation [62.7633180470428]
  Local features provide region-to-region rather than point-to-point correspondences. We propose guidelines for effective use of region-to-region matches in the course of a full model estimation pipeline. Experiments show that affine solvers can achieve accuracy comparable to point-based solvers at faster run-times.
  arXiv  Detail & Related papers  (2020-07-20T12:07:48Z)
• Wise Sliding Window Segmentation: A classification-aided approach for trajectory segmentation [11.174536284571802]
  We propose a supervised trajectory segmentation algorithm called Wise Sliding Window (WS-II) WS-II processes the trajectory coordinates to find behavioral changes in space and time, generating an error signal. We evaluate our method over three real datasets from different domains.
  arXiv  Detail & Related papers  (2020-03-23T12:55:40Z)
• Efficient and Effective Similar Subtrajectory Search with Deep Reinforcement Learning [39.40996178862515]
  We study the SimSub problem and develop a suite of algorithms including both exact and approximate ones. Among those approximate algorithms, two that are based on deep reinforcement learning stand out and outperform those non-learning based algorithms in terms of effectiveness and efficiency. We conduct experiments on real-world trajectory datasets, which verify the effectiveness and efficiency of the proposed algorithms.
  arXiv  Detail & Related papers  (2020-03-05T11:38:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.