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Action Disambiguation Analysis Using Normalized Google-Like Distance Correlogram

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Computer Vision – ACCV 2012 (ACCV 2012)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7726))

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Abstract

Classifying realistic human actions in video remains challenging for existing intro-variability and inter-ambiguity in action classes. Recently, Spatial-Temporal Interest Point (STIP) based local features have shown great promise in complex action analysis. However, these methods have the limitation that they typically focus on Bag-of-Words (BoW) algorithm, which can hardly discriminate actions’ ambiguity due to ignoring of spatial-temporal occurrence relations of visual words. In this paper, we propose a new model to capture this contextual relationship in terms of pairwise features’ co-occurrence. Normalized Google-Like Distance (NGLD) is proposed to numerically measuring this co-occurrence, due to its effectiveness in semantic correlation analysis. All pairwise distances compose a NGLD correlogram and its normalized form is incorporated into the final action representation. It is proved a much richer descriptor by observably reducing action ambiguity in experiments, conducted on WEIZMANN dataset and the more challenging UCF sports. Results also demonstrate the proposed model is more effective and robust than BoW on different setups.

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Author information

Authors and Affiliations

  1. Engineering Lab on Intelligent Perception for Internet Of Things(ELIP), Key Laboratory for Machine Perception, Shenzhen Graduate School, Peking University, China

    Qianru Sun & Hong Liu

Authors
  1. Qianru Sun
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  2. Hong Liu
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Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, Seoul National University, 1 Gwanak-ro, 151-744, Gwanak-gu, 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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Sun, Q., Liu, H. (2013). Action Disambiguation Analysis Using Normalized Google-Like Distance Correlogram. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds) Computer Vision – ACCV 2012. ACCV 2012. Lecture Notes in Computer Science, vol 7726. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37431-9_33

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  • DOI: https://doi.org/10.1007/978-3-642-37431-9_33

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37430-2

  • Online ISBN: 978-3-642-37431-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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