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Multimedia Tools and Applications

, Volume 78, Issue 10, pp 13247–13261 | Cite as

Instance-level object retrieval via deep region CNN

  • Shuhuan Mei
  • Weiqing Min
  • Hua DuanEmail author
  • Shuqiang Jiang
Article
  • 99 Downloads

Abstract

Instance retrieval is a fundamental problem in the multimedia field for its various applications. Since the relevancy is defined at the instance level, it is more challenging comparing to traditional image retrieval methods. Recent advances show that Convolutional Neural Networks (CNNs) offer an attractive method for image feature representations. However, the CNN method extracts features from the whole image, thus the extracted features contain a large amount of background noisy information, leading to poor retrieval performance. To solve the problem, this paper proposed a deep region CNN method with object detection for instance-level object retrieval, which has two phases, i.e., offline Faster R-CNN training and online instance retrieval. First, we train a Faster R-CNN model to better locate the region of the objects. Second, we extract the CNN features from the detected object image region and then retrieve relevant images based on the visual similarity of these features. Furthermore, we utilized three different strategies for feature fusing based on the detected object region candidates from Faster R-CNN. We conduct the experiment on a large dataset: INSTRE with 23,070 object images and additional one million distractor images. Qualitative and quantitative evaluation results have demonstrated the advantage of our proposed method. In addition, we conducted extensive experiments on the Oxford dataset and the experimental results further validated the effectiveness of our proposed method.

Keywords

Faster R-CNN Deep learning Instance-level object retrieval Instre 

Notes

Acknowledgement

This work was supported in part by the National Natural Science Foundation of China (61532018,61322212, 61602437, 61672497, 61472229 and 61202152), in part by the Beijing Municipal Commission of Science and Technology (D161100001816001),in part by Beijing Natural Science Foundation (4174106), in part by the Lenovo Outstanding Young Scientists Program, in part by National Program for Special Support of Eminent Professionals and National Program for Support of Top-notch Young Professionals, and in part by China Postdoctoral Science Foundation (2016M590135, 2017T100110). This work was also supported in part by Science and Technology Development Fund of Shandong Province of China (2016ZDJS02A11 and ZR2017MF027), the Taishan Scholar Climbing Program of Shandong Province, and SDUST Research Fund (2015TDJH102).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Shuhuan Mei
    • 1
    • 2
  • Weiqing Min
    • 2
  • Hua Duan
    • 1
    Email author
  • Shuqiang Jiang
    • 2
    • 3
  1. 1.College of Mathematics and Systems ScienceShandong University of Science and TechnologyQingdaoChina
  2. 2.Key Lab of Intelligent Information ProcessingInstitute of Computing Technology, CASBeijingChina
  3. 3.University of Chinese Academy of SciencesBeijingChina

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