Efficient Interactive Search for Geo-tagged Multimedia Data
Jun Long, Lei Zhu, Chengyuan Zhang, Zhan Yang, Yunwu Lin, Ruipeng, Chen

TL;DR
This paper introduces an efficient interactive search method for geo-tagged multimedia images that combines geographical proximity, visual similarity, and user preferences, utilizing novel algorithms and index structures for large-scale data.
Contribution
It proposes a new framework for interactive geo-tagged image search, including the GI-SUPER search algorithm and GIR-Tree index, to improve search efficiency and user preference learning.
Findings
High performance demonstrated on real datasets
Effective integration of spatial, visual, and preference data
Novel index structure improves search speed
Abstract
Due to the advances in mobile computing and multimedia techniques, there are vast amount of multimedia data with geographical information collected in multifarious applications. In this paper, we propose a novel type of image search named interactive geo-tagged image search which aims to find out a set of images based on geographical proximity and similarity of visual content, as well as the preference of users. Existing approaches for spatial keyword query and geo-image query cannot address this problem effectively since they do not consider these three type of information together for query. In order to solve this challenge efficiently, we propose the definition of interactive top- geo-tagged image query and then present a framework including candidate search stage , interaction stage and termination stage. To enhance the searching efficiency in a large-scale database, we propose…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Image and Video Retrieval Techniques · Data Management and Algorithms · Image Retrieval and Classification Techniques
