Paper Info
Title
Social media driven image retrieval
Abstract
People often try to find an image using a short query and images are usually indexed using short annotations. Matching the query vocabulary with the indexing vocabulary is a difficult problem when little text is available. Textual user generated content in Web 2.0 platforms contains a wealth of data that can help solve this problem. Here we describe how to use Wikipedia and Flickr content to improve this match. The initial query is launched in Flickr and we create a query model based on co-occurring terms. We also calculate nearby concepts using Wikipedia and use these to expand the query. The final results are obtained by ranking the results for the expanded query using the similarity between their annotation and the Flickr model. Evaluation of these expansion and ranking techniques, over the Image CLEF 2010 Wikipedia Collection containing 237,434 images and their multilingual textual annotations, shows that a consistent improvement compared to state of the art methods.
Year
DOI
Venue
2011
10.1145/1991996.1992029
ICMR
Keywords
Field
DocType
difficult problem,expanded query,flickr model,flickr content,initial query,wikipedia collection,social media,short query,query vocabulary,image retrieval,indexing vocabulary,query model,indexation,wikipedia,user generated content
User-generated content,Web search query,Information retrieval,Query expansion,Computer science,Web query classification,Search engine indexing,Image retrieval,Ranking (information retrieval),Vocabulary
Conference
Citations 
PageRank 
References 
18
0.95
18
Authors
2
Name
Order
Citations
PageRank
Adrian Popescu126320.15
Gregory Grefenstette21129147.00