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Learning sparse latent representation and distance metric for image retrieval
conference contribution
posted on 2013-01-01, 00:00 authored by Tu Dinh Nguyen, T Truyen, Quoc-Dinh Phung, Svetha VenkateshSvetha VenkateshThe 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.
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Event
Multimedia and Expo. IEEE International Conference (14th : 2013 : San Jose, California)Pagination
1 - 6Publisher
IEEELocation
San Jose, CaliforniaPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2013-07-15End date
2013-07-19ISBN-13
9781479900152Language
engPublication classification
E1 Full written paper - refereedCopyright notice
2013, IEEETitle of proceedings
ICME 2013 : Proceedings of the 14th IEEE International Conference on Multimedia and ExpoUsage metrics
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