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Abstract
In this paper, we propose a novel approach for instance retrieval. Compared with traditional retrieval pipeline, we first locate several candidate regions of target object with a region proposal network (RPN), instead of exhausting sliding window method. The candidate regions are detected through the trained RPN. Then we obtain the region-wise convolutional feature maps (CFMs) by forwarding them through a ROI pooling layer. Our feature encoding representation builds on the common sense that similar patterns have similar activations on feature maps. The target object is regarded as a combination of several meaningful patterns. In this way, we represent an image with the combination of encoded descriptors corresponding to the subsets of the proposed region. We also implement reranking algorithm to refine the proposed region in local retrieval. Through extensive experiments, we demonstrate the suitability of our feature encoding representation for instance retrieval, achieving comparable performance on both Oxford and Paris buildings benchmarks.
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Acknowledgements
The work was supported by the National Natural Science Foundation of China (Grant No. 91420302) and the National Basic Research Program of China (Grant No. 2015CB856004), and the Key Basic Research Program of Shanghai, China (15JC1400103).
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Authors and Affiliations
Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
Zhiming Ding, Zhengzhong Zhou & Liqing Zhang
- Zhiming Ding
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Correspondence toLiqing Zhang.
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Guangdong University of Technology, Guangzhou, China
Derong Liu
Guangdong University of Technology, Guangzhou, China
Shengli Xie
South China University of Technology, Guangzhou, China
Yuanqing Li
Institute of Automation, Chinese Academy of Sciences, Beijing, China
Dongbin Zhao
King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia
El-Sayed M. El-Alfy
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Ding, Z., Zhou, Z., Zhang, L. (2017). Deep Encoding Features for Instance Retrieval. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10636. Springer, Cham. https://doi.org/10.1007/978-3-319-70090-8_7
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