Rekabsaz, Navid and Bierig, Ralf and Ionescu, Bogdan and Hanbury, Allan and Lupu, Mihai (2015) On the Use of Statistical Semantics for Metadata-Based Social Image Retrieval. 2015 13th International Workshop on Content-Based Multimedia Indexing (CBMI). pp. 1-4. ISSN 1949-3991

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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.

Item Type:	Article
Keywords:	Semantics; Image retrieval; Indexing; Context; Encyclopedias; Correlation;
Academic Unit:	Faculty of Science and Engineering > Computer Science Faculty of Science and Engineering > Research Institutes > Hamilton Institute
Item ID:	15205
Identification Number:	https://doi.org/10.1109/CBMI.2015.7153634
Depositing User:	Ralf Bierig
Date Deposited:	10 Jan 2022 13:26
Journal or Publication Title:	2015 13th International Workshop on Content-Based Multimedia Indexing (CBMI)
Publisher:	IEEE
Refereed:	Yes
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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