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International Conference on Cloud Computing and Security

ICCCS 2018: Cloud Computing and Security pp 476–486Cite as

Composite Descriptors and Deep Features Based Visual Phrase for Image Retrieval

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11068))

Abstract

Local descriptors are very effective features in bag-of-visual-words (BoW) and vector of locally aggregated descriptors (VALD) models for image retrieval. Different kinds of local descriptors represent different visual content. We recognize that spatial contextual information play an important role in image matching, image retrieval and image recognition. Therefore, to explore efficient features, firstly, a new local composite descriptor is proposed, which combines the advantages of SURF and color name (CN) information. Then, VLAD method is used to encode the proposed composite descriptors to a vector. Third, local deep features are extracted and fused with the encoded vector in the image block. Finally, to implement efficient retrieval system, a novel image retrieval framework is organized a novel image retrieval framework is organized based on the proposed feature fusion strategies. The proposed methods areis verified on three benchmark datasets, i.e., Holidays, Oxford5k and Ukbench. Experimental results show that our methods achieves good performance. Eespecially, the mAP and N-S score achieve 0.8281 and 3.5498 on Holidays and Ukbench datasets, respectively.

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Author information

Authors and Affiliations

  1. Institute of Information Science, Beijing Jiaotong University, Beijing, 100081, China

    Yanhong Wang, Yigang Cen, Ruizhen Zhao & Tingting Chai

  2. Key Laboratory of Advanced Information Science and Network Technology of Beijing, Beijing, 100081, China

    Yanhong Wang, Yigang Cen, Ruizhen Zhao & Tingting Chai

  3. College of Mechanical Engineering, Guizhou University, Guiyang, 550025, China

    Linna Zhang

  4. School of Information Engineering, Minzu University of China, Beijing, 100081, China

    Yi Cen

Authors
  1. Yanhong Wang
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  2. Linna Zhang
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  3. Yigang Cen
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  4. Ruizhen Zhao
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  5. Tingting Chai
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  6. Yi Cen
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Corresponding author

Correspondence to Yigang Cen .

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Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Zhaoqing Pan

  3. Department of Computer Science, Purdue University, West Lafayette, IN, USA

    Elisa Bertino

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Wang, Y., Zhang, L., Cen, Y., Zhao, R., Chai, T., Cen, Y. (2018). Composite Descriptors and Deep Features Based Visual Phrase for Image Retrieval. In: Sun, X., Pan, Z., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2018. Lecture Notes in Computer Science(), vol 11068. Springer, Cham. https://doi.org/10.1007/978-3-030-00021-9_43

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  • DOI: https://doi.org/10.1007/978-3-030-00021-9_43

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00020-2

  • Online ISBN: 978-3-030-00021-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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