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<p>With the diversification and complexity of multimedia data on big data, it becomes increasingly important to realize accurate and effective mutual retrieval among cross-media knowledge service data. In this paper, we first improve the structure of cross-media knowledge deep relevance analysis and apply it to cross-media data to construct cross-media relevance learning evaluation metrics. Then deep learning is commonly used for training classification labels or mapping vectors to another vector space by supervision, and with the rapid growth of data size and hardware resources, the advantages of deep learning in handling large-scale complex data will become more and more obvious. According to the experimental scheme to extract the features of the original data of Wikipedia and NUS-WIDE and the comparative analysis of the results based on the CCA extension method, the performance of CMC-DCCA on the dataset is 0.319, 0.338, 0.363, and 0.372, respectively, and it outperforms the other four algorithms. This study constructs a correlation analysis model between different media data to mine the correlations between cross-media data, thus realizing cross-media knowledge discovery service research while spawning more intuitive and concrete multimedia information carriers so that users can obtain more comprehensive information.</p>
</abstract>

With the diversification and complexity of multimedia data on big data, it becomes increasingly important to realize accurate and effective mutual retrieval among cross-media knowledge service data. In this paper, we first improve the structure of cross-media knowledge deep relevance analysis and apply it to cross-media data to construct cross-media relevance learning evaluation metrics. Then deep learning is commonly used for training classification labels or mapping vectors to another vector space by supervision, and with the rapid growth of data size and hardware resources, the advantages of deep learning in handling large-scale complex data will become more and more obvious. According to the experimental scheme to extract the features of the original data of Wikipedia and NUS-WIDE and the comparative analysis of the results based on the CCA extension method, the performance of CMC-DCCA on the dataset is 0.319, 0.338, 0.363, and 0.372, respectively, and it outperforms the other four algorithms. This study constructs a correlation analysis model between different media data to mine the correlations between cross-media data, thus realizing cross-media knowledge discovery service research while spawning more intuitive and concrete multimedia information carriers so that users can obtain more comprehensive information.

Research and Application of Cross-media Knowledge Discovery Service Based on Deep Learning Model

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

With the diversification and complexity of multimedia data on big data, it becomes increasingly important to realize accurate and effective mutual retrieval...

Research and Application of Cross-media Knowledge Discovery Service Based on Deep Learning Model

Pubblicato online: 18 set 2023

Pagine: -

Ricevuto: 29 ott 2022

Accettato: 29 mar 2023

DOI: https://doi.org/10.2478/amns.2023.2.00341

Parole chiave
Big data, Cross-media knowledge, Vector space, Deep learning, CMC-DCCA

© 2023 Hongbo Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research and Application of Cross-media Knowledge Discovery Service Based on Deep Learning Model

Pubblicato online: 18 set 2023

Pagine: -

Ricevuto: 29 ott 2022

Accettato: 29 mar 2023

DOI: https://doi.org/10.2478/amns.2023.2.00341

Parole chiaveBig data, Cross-media knowledge, Vector space, Deep learning, CMC-DCCA

© 2023 Hongbo Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Big data, Cross-media knowledge, Vector space, Deep learning, CMC-DCCA