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Piecewise supervised deep hashing for image retrieval

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Abstract

In this paper, we propose a novel hash code generation method based on convolutional neural network (CNN), called the piecewise supervised deep hashing (PSDH) method to directly use a latent layer data and the output layer result of the classification network to generate a two-segment hash code for every input image. The first part of the hash code is the class information hash code, and the second part is the feature message hash code. The method we proposed is a point-wise approach and it is easy to implement and works very well for image retrieval. In particular, it performs excellently in the search of pictures with similar features. The more similar the images are in terms of color and geometric information and so on, the better it will rank above the search results. Compared with the hashing method proposed so far, we keep the whole hashing code search method, and put forward a piecewise hashing code search method. Experiments on three public datasets demonstrate the superior performance of PSDH over several state-of-art methods.

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Authors and Affiliations

  1. School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China

    Yannuan Li, Ting Fu & Weijun Hu

  2. China School of Software Engineering, Huazhong University of Science and Technology, Wuhan, China

    Lin Wan

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  1. Yannuan Li
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  2. Lin Wan
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  3. Ting Fu
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Correspondence to Lin Wan.

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Li, Y., Wan, L., Fu, T. et al. Piecewise supervised deep hashing for image retrieval. Multimed Tools Appl 78, 24431–24451 (2019). https://doi.org/10.1007/s11042-018-7072-4

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