English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

In the era of big data, methods for improving memory and computationalefficiency have become crucial for successful deployment of technologies.Hashing is one of the most effective approaches to deal with computationallimitations that come with big data. One natural way for formulating thisproblem is spectral hashing that directly incorporates affinity to learn binarycodes. However, due to binary constraints, the optimization becomesintractable. To mitigate this challenge, different relaxation approaches havebeen proposed to reduce the computational load of obtaining binary codes andstill attain a good solution. The problem with all existing relaxation methodsis resorting to one or more additional auxiliary variables to attain highquality binary codes while relaxing the problem. The existence of auxiliaryvariables leads to coordinate descent approach which increases thecomputational complexity. We argue that introducing these variables isunnecessary. To this end, we propose a novel relaxed formulation for spectralhashing that adds no additional variables to the problem. Furthermore, insteadof solving the problem in original space where number of variables is equal tothe data points, we solve the problem in a much smaller space and retrieve thebinary codes from this solution. This trick reduces both the memory andcomputational complexity at the same time. We apply two optimizationtechniques, namely projected gradient and optimization on manifold, to obtainthe solution. Using comprehensive experiments on four public datasets, we showthat the proposed efficient spectral hashing (ESH) algorithm achieves highlycompetitive retrieval performance compared with state of the art at lowcomplexity.

A non-alternating graph hashing algorithm for large scale image search