On the use of statistical semantics for metadata-based social image retrieval

Authors: 
Navid Rekabsaz
Ralf Bierig
Bogdan Ionescu
Allan Hanbury
Type: 
Poster presentation with proceedings
Proceedings: 
On the use of statistical semantics for metadata-based social image retrieval
Publisher: 
IEEE
Pages: 
1 - 4
ISBN: 
Year: 
2015
Abstract: 
We revisit text-based image retrieval for social media, exploring the opportunities offered by statistical semantics. We assess the performance and limitation of several complementary corpus-based semantic text similarity methods in combination with word representations. We compare results with state-of-the-art text search engines. Our deep learning-based semantic retrieval methods show a statistically significant improvement in comparison to a best practice Solr search engine, at the expense of a significant increase in processing time. We provide a solution for reducing the semantic processing time up to 48% compared to the standard approach, while achieving the same performance.
TU Focus: 
Information and Communication Technology
Reference: 

N. Rekabsaz, R. Bierig, B. Ionescu, A. Hanbury, M. Lupu:
"On the use of statistical semantics for metadata-based social image retrieval";
Poster: 13th International Workshop on Content-Based Multimedia Indexing (CBMI 2015), Prague; 10.06.2015 - 12.06.2015; in: "On the use of statistical semantics for metadata-based social image retrieval", IEEE, 15292549 (2015), S. 1 - 4.

Zusätzliche Informationen

Last changed: 
04.01.2016 11:53:33
TU Id: 
245113
Accepted: 
Accepted
Invited: 
Department Focus: 
Business Informatics
Abstract German: 
Author List: 
N. Rekabsaz, R. Bierig, B. Ionescu, A. Hanbury, M. Lupu