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

Cross-modal hashing facilitates mapping of heterogeneous multimedia data intoa common Hamming space, which can beutilized for fast and flexible retrievalacross different modalities. In this paper, we propose a novel cross-modalhashingarchitecture-deep neural decoder cross-modal hashing (DNDCMH), whichuses a binary vector specifying the presence of certainfacial attributes as aninput query to retrieve relevant face images from a database. The DNDCMHnetwork consists of two separatecomponents: an attribute-based deep cross-modalhashing (ADCMH) module, which uses a margin (m)-based loss functiontoefficiently learn compact binary codes to preserve similarity betweenmodalities in the Hamming space, and a neural error correctingdecoder (NECD),which is an error correcting decoder implemented with a neural network. Thegoal of NECD network in DNDCMH isto error correct the hash codes generated byADCMH to improve the retrieval efficiency. The NECD network is trained suchthat it hasan error correcting capability greater than or equal to the margin(m) of the margin-based loss function. This results in NECD cancorrect thecorrupted hash codes generated by ADCMH up to the Hamming distance of m. Wehave evaluated and comparedDNDCMH with state-of-the-art cross-modal hashingmethods on standard datasets to demonstrate the superiority of our method.

Error-Corrected Margin-Based Deep Cross-Modal Hashing for Facial Image Retrieval