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/

Zendy Plus

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

Zendy Tools

Zendy Open

Online hashing methods are efficient in learning the hash functions from thestreaming data. However, when the hash functions change, the binary codes forthe database have to be recomputed to guarantee the retrieval accuracy.Recomputing the binary codes by accumulating the whole database brings atimeliness challenge to the online retrieval process. In this paper, we proposea novel online hashing framework to update the binary codes efficiently withoutaccumulating the whole database. In our framework, the hash functions are fixedand the projection functions are introduced to learn online from the streamingdata. Therefore, inefficient updating of the binary codes by accumulating thewhole database can be transformed to efficient updating of the binary codes byprojecting the binary codes into another binary space. The queries and thebinary code database are projected asymmetrically to further improve theretrieval accuracy. The experiments on two multi-label image databasesdemonstrate the effectiveness and the efficiency of our method for multi-labelimage retrieval.

Online Hashing with Efficient Updating of Binary Codes