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International Conference on Intelligent Computing

ICIC 2017: Intelligent Computing Theories and Application pp 758–767Cite as

Smooth Multi-instance Learning for Object Detection

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10361))

Abstract

The problem of object localization is one of the key problems in computer vision applications. Recently, multiple-instance learning (MIL) is a kind of machine learning framework which receiving a set of instances that are individually labeled. This framework has been verified that will get good effect in object localization in images. In this paper, we propose a novel method to handle the classical MIL problem. We preprocess images with superpixel techniques to speed up the whole procedure of training our model and regard the positiveness of instance as a continuous variable. The softmax model is used to bring a bridge between instances and bags and jointly optimize the bag label and instance label in a unified framework. At last, the model is trained by iterative weakly supervised training method. The extensive experiments demonstrate that out method achieves superior performance on various MIL benchmarks. The state-of-the-art results of object discovery on Pascal VOC datasets further confirm the advantages of the proposed method.

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Acknowledgments

This work was supported by the grants of the National Science Foundation of China, Nos. 61520106006, 31571364, U1611265, 61532008, 61672203, 61402334, 61472282, 61472280, 61472173, 61572447, 61373098 and 61672382, China Postdoctoral Science Foundation Grant, Nos. 2016M601646.

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Authors and Affiliations

  1. School of Electronics and Information Engineering, Tongji University, Caoan Road 4800, Shanghai, 201804, China

    Dayuan Li & Zhipeng Li

  2. School of Information and Computer, Anhui Agricultural University, Changjiang West Road 130, Hefei, Anhui, China

    Youhua Zhang

Authors
  1. Dayuan Li
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  2. Zhipeng Li
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  3. Youhua Zhang
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Corresponding author

Correspondence to Dayuan Li .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Politecnico di Bari, Bari, Italy

    Vitoantonio Bevilacqua

  3. University of Wollongong, North Wollongong, New South Wales, Australia

    Prashan Premaratne

  4. Indian Institute of Technology, Kanpur, India

    Phalguni Gupta

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Li, D., Li, Z., Zhang, Y. (2017). Smooth Multi-instance Learning for Object Detection. In: Huang, DS., Bevilacqua, V., Premaratne, P., Gupta, P. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science(), vol 10361. Springer, Cham. https://doi.org/10.1007/978-3-319-63309-1_67

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  • DOI: https://doi.org/10.1007/978-3-319-63309-1_67

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63308-4

  • Online ISBN: 978-3-319-63309-1

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