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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Region search is widely used for object localisation in computer vision. After projecting the score of an image classifier into an image plane, region search aims to find regions that precisely localise desired objects. The recently proposed region search methods, such as efficient subwindow search and efficient region search, usually find regions with maximal score. For some classifiers and scenarios, the projected scores are nearly all positive or very noisy, then maximising the score of a region results in localising nearly the entire images as objects, or causes localisation results unstable. In this study, the authors observe that the projected scores with large magnitudes are mainly concentrated on or around objects. On the basis of this observation, they propose a region search method for object localisation, named level set maximum‐weight connected subgraph (LS‐MWCS). It localises objects by searching regions by graph mode‐seeking rather than the maximal score. The score density by localised region can be controlled by a parameter flexibly. They also prove an interesting property of the proposed LS‐MWCS, which guarantees that the region with desired density can be found. Moreover, the LS‐MWCS can be efficiently solved by the belief propagation scheme. The effectiveness of the author's method is validated on the problem of weakly‐supervised object localisation. Quantitative results on synthetic and real data demonstrate the superiorities of their method compared to other state‐of‐the‐art methods.

Density‐based region search with arbitrary shape for object localisation