Learning sparse latent representation and distance metric for image retrieval Nguyen, Tu Dinh, Truyen, Tran, Phung, Dinh and Venkatesh, Svetha 2013, Learning sparse latent representation and distance metric for image retrieval, in ICME 2013 : Proceedings of the 14th IEEE International Conference on Multimedia and Expo, IEEE, Piscataway, N.J., pp. 1-6, doi: 10.1109/ICME.2013.6607435. Attached Files Name Description MIMEType Size Downloads Title Learning sparse latent representation and distance metric for image retrieval Author(s) Nguyen, Tu Dinh Truyen, Tran orcid.org/0000-0001-6531-8907 Phung, Dinh orcid.org/0000-0002-9977-8247 Venkatesh, Svetha orcid.org/0000-0001-8675-6631 Conference name Multimedia and Expo. IEEE International Conference (14th : 2013 : San Jose, California) Conference location San Jose, California Conference dates 15-19 Jul. 2013 Title of proceedings ICME 2013 : Proceedings of the 14th IEEE International Conference on Multimedia and Expo Editor(s) [Unknown] Publication date 2013 Conference series IEEE International Conference on Multimedia and Expo Start page 1 End page 6 Total pages 6 Publisher IEEE Place of publication Piscataway, N.J. Keyword(s) image retrieval mixed-variate NUS-WIDE restricted Boltzmann machines metric learning sparsity Summary The performance of image retrieval depends critically on the semantic representation and the distance function used to estimate the similarity of two images. A good representation should integrate multiple visual and textual (e.g., tag) features and offer a step closer to the true semantics of interest (e.g., concepts). As the distance function operates on the representation, they are interdependent, and thus should be addressed at the same time. We propose a probabilistic solution to learn both the representation from multiple feature types and modalities and the distance metric from data. The learning is regularised so that the learned representation and information-theoretic metric will (i) preserve the regularities of the visual/textual spaces, (ii) enhance structured sparsity, (iii) encourage small intra-concept distances, and (iv) keep inter-concept images separated. We demonstrate the capacity of our method on the NUS-WIDE data. For the well-studied 13 animal subset, our method outperforms state-of-the-art rivals. On the subset of single-concept images, we gain 79:5% improvement over the standard nearest neighbours approach on the MAP score, and 45.7% on the NDCG. ISBN 9781479900152 Language eng DOI 10.1109/ICME.2013.6607435 Field of Research 080109 Pattern Recognition and Data Mining Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences HERDC Research category E1 Full written paper - refereed HERDC collection year 2013 Copyright notice ©2013, IEEE Persistent URL http://hdl.handle.net/10536/DRO/DU:30057165 Document type: Conference Paper Collection: Centre for Pattern Recognition and Data Analytics Related Links Link Description Connect to published version (restricted access) Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Wed, 23 Oct 2013, 10:02:51 EST Wed, 23 Oct 2013, 15:01:48 EST Wed, 30 Oct 2013, 11:16:11 EST Wed, 30 Oct 2013, 11:44:55 EST Fri, 01 Nov 2013, 12:57:54 EST Thu, 20 Feb 2014, 07:39:33 EST Wed, 21 Oct 2015, 14:52:36 EST Wed, 21 Oct 2015, 18:23:38 EST Wed, 18 Nov 2015, 07:39:30 EST Wed, 20 Apr 2016, 12:44:12 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 3 times in Scopus Search Google Scholar Access Statistics: 246 Abstract Views, 1 File Downloads  -  Detailed Statistics Created: Wed, 23 Oct 2013, 10:02:51 EST

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