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FBC: A Flat Binary Code Scheme for Fast Manhattan Hash Retrieval

机译：FBC：快速曼哈顿哈希检索的平面二进制代码方案

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摘要

Hash coding is a widely used technique in approximate nearest neighbor (ANN) search, especially in document search and multimedia (such as image and video) retrieval. Based on the difference of distance measurement, hash methods are generally classified into two categories: Hamming hashing and Manhattan hashing. Benefitting from better neighborhood structure preservation, Manhattan hashing methods outperform earlier methods in search effectiveness. However, due to using decimal arithmetic operations instead of bit operations, Manhattan hashing becomes a more time-consuming process, which significantly decreases the whole search efficiency. To solve this problem, we present an intuitive hash scheme which uses Flat Binary Code (FBC) to encode the data points. As a result, the decimal arithmetic used in previous Manhattan hashing can be replaced by more efficient XOR operator. The final experiments show that with a reasonable memory space growth, our FBC speeds up more than 80% averagely without any search accuracy loss when comparing to the state-of-art Manhattan hashing methods.
机译：哈希编码是在近似最近邻居（ANN）搜索中广泛使用的技术，尤其是在文档搜索和多媒体（例如图像和视频）检索中。根据距离测量的差异，哈希方法通常分为两类：汉明哈希和曼哈顿哈希。得益于更好的邻域结构保护，Manhattan哈希方法在搜索效果方面优于早期方法。但是，由于使用十进制算术运算而不是位运算，曼哈顿哈希运算变得更加耗时，从而显着降低了整个搜索效率。为了解决此问题，我们提出了一种直观的哈希方案，该方案使用平面二进制代码（FBC）对数据点进行编码。结果，以前的曼哈顿哈希中使用的十进制算术可以被更有效的XOR运算符代替。最终实验表明，与现有的曼哈顿哈希算法相比，我们的FBC在合理的内存空间增长下平均提高了80％以上，而没有任何搜索精度损失。

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