提出三阶段框架，高效筛选移动视觉众包中的冗余低质图像。
Tri-Select: A Multi-Stage Visual Data Selection Framework for Mobile Visual Crowdsensing
- 基于元数据、空间相似性与视觉特征分三步过滤图像。
- 在真实与公开数据集上提升筛选效率与数据质量。
- 适合大规模移动视觉众包场景，提升数据可用性。
移动视觉众包通过分布式手机设备收集图像，实现大范围精细化环境监测。然而，由于视角重叠、分辨率差异和用户行为多样，数据常具冗余性和异构性。本文提出Tri-Select——一种多阶段视觉数据选择框架，高效过滤冗余与低质量图像。该框架包含三个阶段：(1) 基于元数据的过滤，剔除无关样本；(2) 基于空间相似性的谱聚类，组织候选图像；(3) 基于视觉特征的最大独立集搜索，保留高质量、代表性图像。在真实与公开数据集上的实验表明，Tri-Select显著提升数据筛选效率与数据集质量，适用于可扩展的众包应用。
原文摘要 · Abstract (English)
Mobile visual crowdsensing enables large-scale, fine-grained environmental monitoring through the collection of images from distributed mobile devices. However, the resulting data is often redundant and heterogeneous due to overlapping acquisition perspectives, varying resolutions, and diverse user behaviors. To address these challenges, this paper proposes Tri-Select, a multi-stage visual data selection framework that efficiently filters redundant and low-quality images. Tri-Select operates in three stages: (1) metadata-based filtering to discard irrelevant samples; (2) spatial similarity-based spectral clustering to organize candidate images; and (3) a visual-feature-guided selection based on maximum independent set search to retain high-quality, representative images. Experiments on real-world and public datasets demonstrate that Tri-Select improves both selection efficiency and dataset quality, making it well-suited for scalable crowdsensing applications.
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