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Discrete Spatial Importance-Based Deep Weighted Hashing

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Book cover Computer Vision – ACCV 2020 (ACCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12624))

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Abstract

Hashing is a widely used technique for large-scale approximate nearest neighbor searching in multimedia retrieval. Recent works have proved that using deep neural networks is a promising solution for learning both feature representation and hash codes. However, most existing deep hashing methods directly learn hash codes from a convolutional neural network, ignoring the spatial importance distribution of images. The loss of spatial importance negatively affects the performance of hash learning and thus reduces its accuracy. To address this issue, we propose a new deep hashing method with weighted spatial information, which generates hash codes by using discrete spatial importance distribution. In particular, to extract the discrete spatial importance information of images effectively, we propose a method to learn the spatial attention map and hash code simultaneously, which makes the spatial attention map more conductive to hash-based retrieval. The experimental results of three widely used datasets show that the proposed deep weighted hashing method is superior to the state-of-the-art hashing method.

Y. Yin—This work was supported in part by the National Natural Science Foundation of China (61671274, 61876098), National Key R & D Program of China (2018YFC0830100, 2018YFC0830102) and special funds for distinguished professors of Shandong Jianzhu University.

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Author information

Authors and Affiliations

  1. Shandong University, 1500 Shunhua Road, Jinan, Shandong Province, China

    Yang Shi, Quan Zhou & Yilong Yin

  2. Shandong Jianzhu University, 1000 Fengming Road, Jinan, Shandong Province, China

    Xiushan Nie & Xiaoming Xi

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  1. Yang Shi
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  2. Xiushan Nie
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  3. Quan Zhou
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  4. Xiaoming Xi
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  5. Yilong Yin
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Correspondence to Xiushan Nie or Yilong Yin .

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Editors and Affiliations

  1. Waseda University, Tokyo, Japan

    Hiroshi Ishikawa

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

    Cheng-Lin Liu

  3. Czech Technical University in Prague, Prague, Czech Republic

    Tomas Pajdla

  4. University of Pennsylvania, Philadelphia, PA, USA

    Jianbo Shi

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Shi, Y., Nie, X., Zhou, Q., Xi, X., Yin, Y. (2021). Discrete Spatial Importance-Based Deep Weighted Hashing. In: Ishikawa, H., Liu, CL., Pajdla, T., Shi, J. (eds) Computer Vision – ACCV 2020. ACCV 2020. Lecture Notes in Computer Science(), vol 12624. Springer, Cham. https://doi.org/10.1007/978-3-030-69535-4_23

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  • DOI: https://doi.org/10.1007/978-3-030-69535-4_23

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