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

We propose a new image representation for texture categorization and facialanalysis, relying on the use of higher-order local differential statistics asfeatures. It has been recently shown that small local pixel patterndistributions can be highly discriminative while being extremely efficient tocompute, which is in contrast to the models based on the global structure ofimages. Motivated by such works, we propose to use higher-order statistics oflocal non-binarized pixel patterns for the image description. The proposedmodel does not require either (i) user specified quantization of the space (ofpixel patterns) or (ii) any heuristics for discarding low occupancy volumes ofthe space. We propose to use a data driven soft quantization of the space, withparametric mixture models, combined with higher-order statistics, based onFisher scores. We demonstrate that this leads to a more expressiverepresentation which, when combined with discriminatively learned classifiersand metrics, achieves state-of-the-art performance on challenging texture andfacial analysis datasets, in low complexity setup. Further, it is complementaryto higher complexity features and when combined with them improves performance.

Local Higher-Order Statistics (LHS) describing images with statistics of local non-binarized pixel patterns