Geo-Temporal Distribution of Tag Terms for Event-Related Image Retrieval

TL;DR

This paper introduces a novel geo-temporal feature extraction method for event-related image retrieval that leverages spatial and temporal patterns of user tags to enhance query expansion and improve retrieval accuracy.

Contribution

It proposes a new approach to extract geo-spatial features from user tags and combines them with temporal features for better query expansion in image retrieval systems.

Findings

Geo-spatial features significantly improve retrieval performance.

Combining spatial and temporal features yields better results than using either alone.

The method is effective and viable based on experimental evaluations.

Abstract

Media sharing applications, such as Flickr and Panoramio, contain a large amount of pictures related to real life events. For this reason, the development of effective methods to retrieve these pictures is important, but still a challenging task. Recognizing this importance, and to improve the retrieval effectiveness of tag-based event retrieval systems, we propose a new method to extract a set of geographical tag features from raw geo-spatial profiles of user tags. The main idea is to use these features to select the best expansion terms in a machine learning-based query expansion approach. Specifically, we apply rigorous statistical exploratory analysis of spatial point patterns to extract the geo-spatial features. We use the features both to summarize the spatial characteristics of the spatial distribution of a single term, and to determine the similarity between the spatial profiles of two terms -- i.e., term-to-term spatial similarity. To further improve our approach, we investigate the effect of combining our geo-spatial features with temporal features on choosing the expansion terms. To evaluate our method, we perform several experiments, including well-known feature analyses. Such analyses show how much our proposed geo-spatial features contribute to improve the overall retrieval performance. The results from our experiments demonstrate the effectiveness and viability of our method.