Deep Region Hashing for Generic Instance Search from Images

Authors

  • Jingkuan Song University of Electronic Science and Technology of China
  • Tao He University of Electronic Science and Technology of China
  • Lianli Gao University of Electronic Science and Technology of China
  • Xing Xu University of Electronic Science and Technology of China
  • Heng Tao Shen University of Electronic Science and Technology of China

DOI:

https://doi.org/10.1609/aaai.v32i1.11277

Keywords:

hashing, region, image retrieval

Abstract

Instance Search (INS) is a fundamental problem for many applications, while it is more challenging comparing to traditional image search since the relevancy is defined at the instance level. Existing works have demonstrated the success of many complex ensemble systems that are typically conducted by firstly generating object proposals, and then extracting handcrafted and/or CNN features of each proposal for matching. However, object bounding box proposals and feature extraction are often conducted in two separated steps, thus the effectiveness of these methods collapses. Also, due to the large amount of generated proposals, matching speed becomes the bottleneck that limits its application to large-scale datasets. To tackle these issues, in this paper we propose an effective and efficient Deep Region Hashing (DRH) approach for large-scale INS using an image patch as the query. Specifically, DRH is an end-to-end deep neural network which consists of object proposal, feature extraction, and hash code generation. DRH shares full-image convolutional feature map with the region proposal network, thus enabling nearly cost-free region proposals. Also, each high-dimensional, real-valued region features are mapped onto a low-dimensional, compact binary codes for the efficient object region level matching on large-scale dataset. Experimental results on four datasets show that our DRH can achieve even better performance than the state-of-the-arts in terms of mAP, while the efficiency is improved by nearly 100 times.

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Published

2018-04-25

How to Cite

Song, J., He, T., Gao, L., Xu, X., & Shen, H. T. (2018). Deep Region Hashing for Generic Instance Search from Images. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). https://doi.org/10.1609/aaai.v32i1.11277