Cross-media relevance mining for evaluating text-based image search engine

Xu, Z; Yang, Y; Kassim, A; Yan, S

Cross-media relevance mining for evaluating text-based image search engine

Xu, Z Yang, Y

Kassim, A Yan, S

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Publication Type:: Conference Proceeding
Citation:: 2014 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2014, 2014
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Xu, Z	en_US
dc.contributor.author	Yang, Y https://orcid.org/0000-0001-5528-0546	en_US
dc.contributor.author	Kassim, A	en_US
dc.contributor.author	Yan, S	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	2014 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2014, 2014	en_US
dc.identifier.isbn	9781479947171	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120257
dc.description.abstract	© 2014 IEEE. Targeted at MSR-Bing Image Retrieval grand challenge, we accumulate the experience from the one in 2013, and the make further investigation into different models to solve the relevance assessment problem. Generally speaking, the assessment of relevance between text query and image list is very hard due to the semantic gap. It's not easy to find the 'mapping' from text query into the visual world. Solutions from 2013 MSR-Bing grand challenge are discussed in this paper. Combining with our own observation, we give some insights into why some solutions work well, while others not. Our main solution is to combine the deep learning features with the wining solution of last year (average similarity measurement and page rank), since the deep learning features have more compact representation than the traditional BoWs features, and deep learning features are efficient (on a descent GPU) with very good performance. Our solution achieved the 1st place in MSR-Bing grand challenge 2014. Finally, we give the running time of our solution in the testing phase for the 2014 ICME testing set and development set, respectively.	en_US
dc.relation.ispartof	2014 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2014	en_US
dc.relation.isbasedon	10.1109/ICMEW.2014.6890606	en_US
dc.title	Cross-media relevance mining for evaluating text-based image search engine	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2014 IEEE. Targeted at MSR-Bing Image Retrieval grand challenge, we accumulate the experience from the one in 2013, and the make further investigation into different models to solve the relevance assessment problem. Generally speaking, the assessment of relevance between text query and image list is very hard due to the semantic gap. It's not easy to find the 'mapping' from text query into the visual world. Solutions from 2013 MSR-Bing grand challenge are discussed in this paper. Combining with our own observation, we give some insights into why some solutions work well, while others not. Our main solution is to combine the deep learning features with the wining solution of last year (average similarity measurement and page rank), since the deep learning features have more compact representation than the traditional BoWs features, and deep learning features are efficient (on a descent GPU) with very good performance. Our solution achieved the 1st place in MSR-Bing grand challenge 2014. Finally, we give the running time of our solution in the testing phase for the 2014 ICME testing set and development set, respectively.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/120257