CRH: A Simple Benchmark Approach to Continuous Hashing

TL;DR

CRH introduces a simple, adaptive continuous hashing method for sequential data streams that avoids iteration and effectively encodes streaming media, demonstrating outstanding performance as a benchmark approach.

Contribution

The paper proposes CRH, a novel continuous hashing technique that adaptively encodes data streams without prior knowledge or iterative learning, simplifying the process.

Findings

Outperforms existing methods in encoding streaming media

Provides a benchmark approach for continuous hashing

Efficiently approximates differential encoding patterns

Abstract

In recent years, the distinctive advancement of handling huge data promotes the evolution of ubiquitous computing and analysis technologies. With the constantly upward system burden and computational complexity, adaptive coding has been a fascinating topic for pattern analysis, with outstanding performance. In this work, a continuous hashing method, termed continuous random hashing (CRH), is proposed to encode sequential data stream, while ignorance of previously hashing knowledge is possible. Instead, a random selection idea is adopted to adaptively approximate the differential encoding patterns of data stream, e.g., streaming media, and iteration is avoided for stepwise learning. Experimental results demonstrate our method is able to provide outstanding performance, as a benchmark approach to continuous hashing.