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Supervised Object Boundary Detection Based on Structured Forests

  • Conference paper
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Data Science (ICDS 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9208))

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Abstract

Object boundary detection is an interesting and challenging topic in computer vision. Learning and combining the local, mid-level and high-level information play an important role in most of the recent approaches. However, few characteristics of a certain type of object are exploited. In this paper, we propose a novel supervised machine learning framework for object boundary detection, which makes use of the specific object features, such as boundary shape, directions and intensity. In the learning process, structured forest models are employed to tackle the high dimensional multi-class problem. Various experiment results show that our framework outperforms the competing models in the proposed data set, indicating that our framework is highly effective in modeling boundary for specific type of objects.

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Acknowledgments

This work is partly supported by National Natural Science Foundation of China under Grants (Grant No. 71331005, 71110107026, 61402429). I would like to express my gratitude to my supervisor Prof. Shi and Dr. Qi who helped me a lot in studying and everyday life. I also would like to thank Jason and Limeng for the inspiring discussions and suggestions. Last my thanks would go to my girlfriend Jing, who makes my life more colorful.

Author information

Authors and Affiliations

  1. Key Laboratory of Big Data Mining and Knowledge Management, CAS, Beijing, 100190, China

    Fan Meng, Zhiquan Qi, Limeng Cui, Zhensong Chen & Yong Shi

  2. Research Center on Fictitious Economy and Data Science, CAS, Beijing, 100190, China

    Fan Meng, Zhiquan Qi, Limeng Cui, Zhensong Chen & Yong Shi

  3. School of Management, University of Chinese Academy Sciences, Beijing, 100190, China

    Fan Meng, Zhensong Chen & Yong Shi

  4. School of Computer and Control Engineering, University of Chinese Academy Sciences, Beijing, 100190, China

    Limeng Cui

Authors
  1. Fan Meng
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  2. Zhiquan Qi
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  3. Limeng Cui
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  4. Zhensong Chen
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  5. Yong Shi
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Corresponding authors

Correspondence to Zhiquan Qi or Yong Shi .

Editor information

Editors and Affiliations

  1. University of Technology, Sydney, Australia

    Chengqi Zhang

  2. Xi'an Jiaotong University, Xi'an Jiaotong, China

    Wei Huang

  3. Chinese Academy of Sciences, Beijing, China

    Yong Shi

  4. University of Illinois Chicago, CHICAGO, Illinois, USA

    Philip S. Yu

  5. Fudan University, Shanghai, China

    Yangyong Zhu

  6. Research Center on Fictitious Econo, Chinese Academy of Sciences, Beijing, China

    Yingjie Tian

  7. University of Technology, Sydney, Australia

    Peng Zhang

  8. Victoria University, Melbourne, Victoria, Australia

    Jing He

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© 2015 Springer International Publishing Switzerland

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Meng, F., Qi, Z., Cui, L., Chen, Z., Shi, Y. (2015). Supervised Object Boundary Detection Based on Structured Forests. In: Zhang, C., et al. Data Science. ICDS 2015. Lecture Notes in Computer Science(), vol 9208. Springer, Cham. https://doi.org/10.1007/978-3-319-24474-7_13

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  • DOI: https://doi.org/10.1007/978-3-319-24474-7_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24473-0

  • Online ISBN: 978-3-319-24474-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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