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

With the rapid advance of Internet technology and smart devices, users oftenneed to manage large amounts of multimedia information using smart devices,such as personal image and video accessing and browsing. These requirementsheavily rely on the success of image (video) annotation, and thus large scaleimage annotation through innovative machine learning methods has attractedintensive attention in recent years. One representative work is support vectormachine (SVM). Although it works well in binary classification, SVM has anon-smooth loss function and can not naturally cover multi-class case. In thispaper, we propose manifold regularized kernel logistic regression (KLR) for webimage annotation. Compared to SVM, KLR has the following advantages: (1) theKLR has a smooth loss function; (2) the KLR produces an explicit estimate ofthe probability instead of class label; and (3) the KLR can naturally begeneralized to the multi-class case. We carefully conduct experiments on MIRFLICKR dataset and demonstrate the effectiveness of manifold regularized kernellogistic regression for image annotation.

Manifold regularized kernel logistic regression for web image annotation