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Journal of Applied Sciences >

2017 , Vol. 35 >Issue 2: 193 - 206

DOI: https://doi.org/10.3969/j.issn.0255-8297.2017.02.006

Euclidean Double Bits Embedding Hashing for Image Retrieval

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  • 1. Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China;
    2. Key Laboratory of Information Science and Network Technology, Beijing Jiaotong University Beijing 100044, China;
    3. College of Sciences, Beijing University of Chemical Technology, Beijing 100029, China

Received date: 2016-08-20

  Revised date: 2016-10-18

  Online published: 2017-03-30

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Abstract

We propose a double-bit embedding hashing method based on the Euclidean distance (DBE-E). Euclidean distance is used to measure similarity between the binary hash codes to better preserve similarity relations of the original feature space and improve retrieval precision. To speed computation, bit operation is used to calculate the Euclidean distance between the hash codes. It is 400 times faster than the traditional calculation method of the Euclidean distance for double-bit embedding of 64-bit hash code. Experiments on three image data sets show that the proposed method produces better results than other popular quantization strategies of hashing.

Key words： hashing; double-bit embedding; image retrieval; Euclidean distance

Cite this article

LI Lei, CEN Yi-gang, ZHAO Rui-zhen, CUI Li-hong, WANG Yan-hong . Euclidean Double Bits Embedding Hashing for Image Retrieval[J]. Journal of Applied Sciences, 2017 , 35(2) : 193 -206 . DOI: 10.3969/j.issn.0255-8297.2017.02.006

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