Talks and Poster Presentations (with Proceedings-Entry):

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; 2015-06-10 - 2015-06-12; in: "On the use of statistical semantics for metadata-based social image retrieval", IEEE, 15292549 (2015), 1 - 4.

English 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.

image retrieval;learning (artificial intelligence);social networking (online);statistical analysis;text analysis;Solr search engine;corpus-based semantic text similarity methods;deep learning-based semantic retrieval methods;metadata-based social image re

"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)

Electronic version of the publication:

Created from the Publication Database of the Vienna University of Technology.