Tri-Select: A Multi-Stage Visual Data Selection Framework for Mobile Visual Crowdsensing

TL;DR

Tri-Select is a multi-stage framework that efficiently filters and selects high-quality, representative images from large, heterogeneous visual crowdsensing datasets, enhancing data quality and scalability.

Contribution

This paper introduces Tri-Select, a novel multi-stage visual data selection framework specifically designed for mobile visual crowdsensing applications.

Findings

Improves selection efficiency in large-scale datasets

Enhances dataset quality by filtering redundant and low-quality images

Demonstrates effectiveness on real-world and public datasets

Abstract

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.