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Learning a Context Aware Dictionary for Sparse Representation

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Computer Vision – ACCV 2012 (ACCV 2012)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7725))

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Abstract

Recent successes in the use of sparse coding for many computer vision applications have triggered the attention towards the problem of how an over-complete dictionary should be learned from data. This is because the quality of a dictionary greatly affects performance in many respects, including computational. While so far the focus has been on learning compact, reconstructive, and discriminative dictionaries, in this work we propose to retain the previous qualities, and further enhance them by learning a dictionary that is able to predict the contextual information surrounding a sparsely coded signal. The proposed framework leverages the K-SVD for learning, fully inheriting its benefits of simplicity and efficiency. A model of structured prediction is designed around this approach, which leverages contextual information to improve the combined recognition and localization of multiple objects from multiple classes within one image. Results on the PASCAL VOC 2007 dataset are in line with the state-of-the-art, and clearly indicate that this is a viable approach for learning a context aware dictionary for sparse representation.

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Author information

Authors and Affiliations

  1. Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV, 26506, USA

    Farzad Siyahjani & Gianfranco Doretto

Authors
  1. Farzad Siyahjani
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  2. Gianfranco Doretto
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Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, 151-744, Seoul, Korea

    Kyoung Mu Lee

  2. Microsoft Research Asia, No. 5, Danling st., Haidian district, 100080, Beijing, P.R. China

    Yasuyuki Matsushita

  3. School of Interactive Computing, Georgia Institute of Technology, 801 Atlantic Drive, CCB 315, 30332, Atlanta, GA, USA

    James M. Rehg

  4. Institute of Automation, National Laboratory of Pattern Recognition, Chinese Academy of Sciences, Zhong Quan Cun East Road 95, Haidian District, 100 190, Beijing, P.R. China

    Zhanyi Hu

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Siyahjani, F., Doretto, G. (2013). Learning a Context Aware Dictionary for Sparse Representation. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds) Computer Vision – ACCV 2012. ACCV 2012. Lecture Notes in Computer Science, vol 7725. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37444-9_18

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  • DOI: https://doi.org/10.1007/978-3-642-37444-9_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37443-2

  • Online ISBN: 978-3-642-37444-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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