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Constructing Hierarchical Visual Tree for Discriminative Image Representation and Classification

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Advances in Multimedia Information Processing – PCM 2013 (PCM 2013)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8294))

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Abstract

To support large-scale visual recognition, it is critical to train a large number of classifiers with high discrimination power. To achieve this task, in this paper a hierarchical visual tree is constructed for organizing a large number of object classes and image concepts according to their inter-concept visual correlations. Based on the hierarchical visual tree, a novel approach is proposed for learning multi-scale group-based dictionary to support discriminative bag-of-visual-words (BoW-based) image representation. In addition, a structural learning approach is developed to enable large-scale classifier training over such hierarchical visual tree. We have also compared the performance of our hierarchical visual tree with traditional label tree over large-scale image collections.

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Lei, H., Zhou, N., Mei, K., Dong, P., Fan, J. (2013). Constructing Hierarchical Visual Tree for Discriminative Image Representation and Classification. In: Huet, B., Ngo, CW., Tang, J., Zhou, ZH., Hauptmann, A.G., Yan, S. (eds) Advances in Multimedia Information Processing – PCM 2013. PCM 2013. Lecture Notes in Computer Science, vol 8294. Springer, Cham. https://doi.org/10.1007/978-3-319-03731-8_59

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  • DOI: https://doi.org/10.1007/978-3-319-03731-8_59

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-03730-1

  • Online ISBN: 978-3-319-03731-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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