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

Hashing algorithm has attracted great attention in recent years. In order to improve the query speed and retrieval accuracy, this paper proposes an adaptive and asymmetric residual hash (AASH) algorithm based on residual hash, integrated learning, and asymmetric pairwise loss. The specific description of the AASH algorithm is as follows: 1) the integrated learning model is proposed based on transfer learning and multi-feature fusion strategy to learn the database hash code; 2) the residual hash model is proposed based on ResNet-50 to learn the query image hash code; 3) the asymmetric pairwise loss is proposed and the parameters of the residual hash model is optimized based on the database hash code; 4) the algorithm learns the database hash code and the query image hash code in an asymmetric manner, and integrates the feature learning part and the hash-coded part in one frame. The experimental results on three different datasets fully demonstrate that the proposed AASH method has better performance than most symmetric and asymmetric deep hash algorithms. Specifically, the optimal result of the AASH algorithm is 0.971 on Cifar10 when the hyperparameter is 100 and the hash code length is 32. The optimal result of the AASH algorithm is 0.945 on ceil images when the hyperparameter is 10 and the hash code length is 24. The optimal result of the AASH algorithm is 0.945 on FD-XJ when the hyperparameter is 15 and the hash code length is 32. In addition, the algorithm verifies convergence, time loss, and effectiveness.

An Adaptive and Asymmetric Residual Hash for Fast Image Retrieval