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    Learning sparse latent representation and distance metric for image retrieval

    Access Status
    Fulltext not available
    Authors
    Nguyen, T.
    Tran, The Truyen
    Phung, D.
    Venkatesh, S.
    Date
    2013
    Type
    Conference Paper
    
    Metadata
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    Citation
    Nguyen, T. and Tran, T.T. and Phung, D. and Venkatesh, S. 2013. Learning sparse latent representation and distance metric for image retrieval, in Proceedings of the International Conference on Multimedia and Expo (ICME), Jul 13-19 2013, pp. 1-6. San Jose, CA: IEEE.
    Source Title
    Proceedings - IEEE International Conference on Multimedia and Expo
    DOI
    10.1109/ICME.2013.6607435
    ISBN
    9781479900152
    School
    Multi-Sensor Proc & Content Analysis Institute
    URI
    http://hdl.handle.net/20.500.11937/19160
    Collection
    • Curtin Research Publications
    Abstract

    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.

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