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Chinese Conference on Pattern Recognition

CCPR 2016: Pattern Recognition pp 259–273Cite as

Efficient Image Retrieval via Feature Fusion and Adaptive Weighting

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 663))

Abstract

In the community of content-based image retrieval (CBIR), single feature only describes specific aspect of image content, resulting in false positive matches prevalently returned as candidate retrieval results with low precision and recall. Typically, frequently-used SIFT feature only depicts local gradient distribution within ROIs of gray scale images lacking color information, and tends to produce limited retrieval performance. In order to tackle such problems, we propose a feature fusion method of integrating multiple diverse image features to gain more complementary and helpful image information. Furthermore, to represent the disparate powers of discrimination of image features, a dynamically updating Adaptive Weights Allocation Algorithm (AWAA) which rationally allocates fusion weights proportional to their contributions to matching is proposed in the paper. Extensive experiments on several benchmark datasets demonstrate that feature fusion simultaneously with adaptive weighting based image retrieval yields more accurate and robust retrieval results than conventional retrieval schema.

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Acknowledgments

This work was supported by National Natural Science Foundation of China (61170185), Aerospace Science Foundation of China (2013ZC54011), Liaoning Province Doctor Startup Fund (20121034), and Program Funded by Liaoning Province Education Administration (L2014070).

Author information

Authors and Affiliations

  1. School of Computer, Shenyang Aerospace University, Shenyang, 110136, China

    Xiangbin Shi & Deyuan Zhang

  2. Key Laboratory of Liaoning General Aviation Academy, Shenyang, 110136, China

    Xiangbin Shi & Deyuan Zhang

  3. School of Information, Liaoning University, Shenyang, 110036, China

    Xiangbin Shi & Zhongqiang Guo

Authors
  1. Xiangbin Shi
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  2. Zhongqiang Guo
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  3. Deyuan Zhang
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Corresponding author

Correspondence to Xiangbin Shi .

Editor information

Editors and Affiliations

  1. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  2. Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, China

    Xuelong Li

  3. Chinese Academy of Sciences, Institute of Computing Technology, Beijing, China

    Xilin Chen

  4. Tsinghua University , Beijing, China

    Jie Zhou

  5. Nanjing University of Science and Technology, Nanjing, China

    Jian Yang

  6. University of Electronic Science and Technology, Chengdu, Sichuan, China

    Hong Cheng

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© 2016 Springer Nature Singapore Pte Ltd.

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Shi, X., Guo, Z., Zhang, D. (2016). Efficient Image Retrieval via Feature Fusion and Adaptive Weighting. In: Tan, T., Li, X., Chen, X., Zhou, J., Yang, J., Cheng, H. (eds) Pattern Recognition. CCPR 2016. Communications in Computer and Information Science, vol 663. Springer, Singapore. https://doi.org/10.1007/978-981-10-3005-5_22

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  • DOI: https://doi.org/10.1007/978-981-10-3005-5_22

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3004-8

  • Online ISBN: 978-981-10-3005-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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