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Arbitrary-Shape Object Localization Using Adaptive Image Grids

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Computer Vision – ACCV 2012 (ACCV 2012)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7724))

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Abstract

Sliding-window based search is a widely used technique for object localization. However, for objects of non-rectangle shapes, noises in windows may mislead the localization, causing unsatisfactory results. In this paper, we propose an efficient bottom-up approach for detecting arbitrary-shape objects using image grids as basic components. First, a test image is partitioned into n×n grids and the object is localized by finding a set of connected grids which maximize the classifier’s response. Then, graph cut segmentation is used to improve the object boundary by utilizing local image context. Instead of using bounding boxes, the proposed approach searches connected regions of any shapes. With the graph cut refinement, our approach can start with coarse image grids and is robust to noises. To make image grids better cover the object of arbitrary shape, we also propose a fast adaptive grid partition method which takes image content into account and can be efficiently implemented by dynamic programming. The use of adaptive partition further improves the localization accuracy of our approach. Experiments on PASCAL VOC 2007 and VOC 2008 datasets demonstrate the effectiveness of our approach.

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Author information

Authors and Affiliations

  1. School of EEE, Nanyang Technology University, Singapore

    Chunluan Zhou & Junsong Yuan

Authors
  1. Chunluan Zhou
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  2. Junsong Yuan
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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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© 2013 Springer-Verlag Berlin Heidelberg

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Zhou, C., Yuan, J. (2013). Arbitrary-Shape Object Localization Using Adaptive Image Grids. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds) Computer Vision – ACCV 2012. ACCV 2012. Lecture Notes in Computer Science, vol 7724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37331-2_6

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  • DOI: https://doi.org/10.1007/978-3-642-37331-2_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37330-5

  • Online ISBN: 978-3-642-37331-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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