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 Plus annual

Presents the access of premium content as premium feature

Premium Content

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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Sparse representations have been successfully applied to signal processing,computer vision and machine learning. Currently there is a trend to learnsparse models directly on structure data, such as region covariance. However,such methods when combined with region covariance often require complexcomputation. We present an approach to transform a structured sparse modellearning problem to a traditional vectorized sparse modeling problem byconstructing a Euclidean space representation for region covariance matrices.Our new representation has multiple advantages. Experiments on several visiontasks demonstrate competitive performance with the state-of-the-art methods.

Parameterizing Region Covariance: An Efficient Way To Apply Sparse Codes On Second Order Statistics