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

Salient object detection plays an important part in a vision system to detectimportant regions. Convolutional neural network (CNN) based methods directlytrain their models with large-scale datasets, but what is the crucial featurefor saliency is still a problem. In this paper, we establish a novel bottom-upfeature named convex hull overlap (CHO), combining with appearance contrastfeatures, to detect salient objects. CHO feature is a kind of enhanced Gestaltcue. Psychologists believe that surroundedness reflects objects overlaprelationship. An object which is on the top of the others is attractive. Ourmethod significantly differs from other earlier works in (1) We set up ahand-crafted feature to detect salient object that our model does not need tobe trained by large-scale datasets; (2) Previous works only focus on appearancefeatures, while our CHO feature makes up the gap between the spatial objectcovering and the object's saliency. Our experiments on a large number of publicdatasets have obtained very positive results.

Salient Object Detection with Convex Hull Overlap