CRH: A Simple Benchmark Approach to Continuous Hashing

Miao Cheng; Ah Chung Tsoi

doi:10.1109/globalsip.2015.7418363

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arXiv · 1810.05730

CRH: A Simple Benchmark Approach to Continuous Hashing

Miao Cheng·Ah Chung Tsoi

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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.

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BibTeX RIS

Miao Cheng, Ah Chung Tsoi. 2018-10-10. CRH: A Simple Benchmark Approach to Continuous Hashing. https://doi.org/10.1109/globalsip.2015.7418363

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