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Scene Recognition via Bi-enhanced Knowledge Space Learning

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New Trends in Computer Technologies and Applications (ICS 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1013))

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Abstract

Scene recognition is one of the hallmark tasks in computer vision, as it provides rich information beyond object recognition and action recognition. It is easy to accept that scene images from the same class always include the same essential objects and relations, for example, scene images of “wedding” usually have bridegroom and bride next to him. Following this observation, we introduce a novel idea to boost the accuracy of scene recognition by mining essential scene sub-graph and learning a bi-enhanced knowledge space. The essential scene sub-graph describes the essential objects and their relations for each scene class. The learned knowledge space is bi-enhanced by global representation on the entire image and local representation on the corresponding essential scene sub-graph. Experimental results on the constructed dataset called Scene 30 demonstrate the effectiveness of our proposed method.

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Acknowledgement

This work is supported by the National Key Research & Development Plan of China (No. 2017YFB1002800), by the National Natural Science Foundation of China under Grant 61872424, 61572503, 61720106006, 61432019, and by NUPTSF (No. NY218001), also supported by the Key Research Program of Frontier Sciences, CAS, Grant NO. QYZDJ-SSW-JSC039, and the K.C. Wong Education Foundation.

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Authors and Affiliations

  1. Hefei University of Technology, Hefei, China

    Jin Zhang & Changsheng Xu

  2. Nanjing University of Posts and Telecommunications, Nanjing, China

    Bing-Kun Bao

  3. National Lab of Pattern Recognition, Institute of Automation, Beijing, China

    Bing-Kun Bao & Changsheng Xu

Authors
  1. Jin Zhang
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  2. Bing-Kun Bao
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  3. Changsheng Xu
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Corresponding author

Correspondence to Bing-Kun Bao .

Editor information

Editors and Affiliations

  1. National Yunlin University of Science and Technology, Douliu, Taiwan

    Chuan-Yu Chang

  2. National Yunlin University of Science and Technology, Douliu, Taiwan

    Chien-Chou Lin

  3. Southern Taiwan University of Science and Technology, Tainan, Taiwan

    Horng-Horng Lin

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Zhang, J., Bao, BK., Xu, C. (2019). Scene Recognition via Bi-enhanced Knowledge Space Learning. In: Chang, CY., Lin, CC., Lin, HH. (eds) New Trends in Computer Technologies and Applications. ICS 2018. Communications in Computer and Information Science, vol 1013. Springer, Singapore. https://doi.org/10.1007/978-981-13-9190-3_23

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  • DOI: https://doi.org/10.1007/978-981-13-9190-3_23

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-9189-7

  • Online ISBN: 978-981-13-9190-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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