基于欧氏距离双比特嵌入哈希的图像检索

doi:10.3969/j.issn.0255-8297.2017.02.006

Abstract

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

CLC Number:

TP391

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.

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Euclidean Double Bits Embedding Hashing for Image Retrieval

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