基于欧氏距离双比特嵌入哈希的图像检索

Euclidean Double Bits Embedding Hashing for Image Retrieval

提出一种基于欧氏距离的双比特嵌入哈希算法,以欧氏距离来度量二进制哈希编码之间的相似性.该方法可更好地保持原始特征空间的相似性关系,提高检索精度.另外,为了提高欧氏距离的计算速度,利用位操作实现二进制哈希编码欧氏距离的计算.对于64位的双比特嵌入哈希码,所提算法比传统欧氏距离的计算速度快400倍左右.在3个主流图像库上进行图像检索实验,与当前主流量化算法相比,该算法取得了更好的检索结果.

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.