Paper Info
Title
Benchmarking Image Retrieval Diversification Techniques for Social Media
Abstract
Image retrieval has been an active research domain for over 30 years and historically it has focused primarily on precision as an evaluation criterion. Similar to text retrieval, where the number of indexed documents became large and many relevant documents exist, it is of high importance to highlight diversity in the search results to provide better results for the user. The Retrieving Diverse Social Images Task of the MediaEval benchmarking campaign has addressed exactly this challenge of retrieving diverse and relevant results for the past years, specifically in the social media context. Multimodal data (e.g., images, text) was made available to the participants including metadata assigned to the images, user IDs, and precomputed visual and text descriptors. Many teams have participated in the task over the years. The large number of publications employing the data and also citations of the overview articles underline the importance of this topic. In this paper, we introduce these publicly available data resources as well as the evaluation framework, and provide an in-depth analysis of the crucial aspects of social image search diversification, such as the capabilities and the evolution of existing systems. These evaluation resources will help researchers for the coming years in analyzing aspects of multimodal image retrieval and diversity of the search results.
Year
DOI
Venue
2021
10.1109/TMM.2020.2986579
IEEE Transactions on Multimedia
Keywords
DocType
Volume
Diversification of image search results,social media information retrieval,multi-modal content description,MediaEval benchmarking initiative
Journal
23
ISSN
Citations 
PageRank 
1520-9210
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Bogdan Ionescu145856.67
Maia Rohm200.34
Bogdan Boteanu3424.28
Alexandru-Lucian Gînsca48010.04
Mihai Lupu528137.27
Henning Müller62538218.89