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

Similarity search is critical for many database applications, including theincreasingly popular online services for Content-Based Multimedia Retrieval(CBMR). These services, which include image search engines, must handle anoverwhelming volume of data, while keeping low response times. Thus,scalability is imperative for similarity search in Web-scale applications, butmost existing methods are sequential and target shared-memory machines. Here weaddress these issues with a distributed, efficient, and scalable index based onLocality-Sensitive Hashing (LSH). LSH is one of the most efficient and populartechniques for similarity search, but its poor referential locality propertieshas made its implementation a challenging problem. Our solution is based on awidely asynchronous dataflow parallelization with a number of optimizationsthat include a hierarchical parallelization to decouple indexing and datastorage, locality-aware data partition strategies to reduce message passing,and multi-probing to limit memory usage. The proposed parallelization attainedan efficiency of 90% in a distributed system with about 800 CPU cores. Inparticular, the original locality-aware data partition reduced the number ofmessages exchanged in 30%. Our parallel LSH was evaluated using the largestpublic dataset for similarity search (to the best of our knowledge) with $10^9$128-d SIFT descriptors extracted from Web images. This is two orders ofmagnitude larger than datasets that previous LSH parallelizations could handle.

Scalable Locality-Sensitive Hashing for Similarity Search in High-Dimensional, Large-Scale Multimedia Datasets