CSRNCVA: a model of cross-media semantic retrieval based on neural computing of visual and auditory sensations

Title:

CSRNCVA: a model of cross-media semantic retrieval based on neural computing of visual and auditory sensations

Creator:

Liu, Yang , Cai, Kun , Li, Chun, and Zheng, Fengbin

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:ce7c074b-f699-44f6-b628-17ff57cc63c6
uuid:ce7c074b-f699-44f6-b628-17ff57cc63c6
doi:10.14311/NNW.2018.28.018

Subject:

cross-media cognitive neural computing, cross-media semantic retrieval, deep belief network, hierarchical temporal memory, probabilistic graphical model, and hierarchical reinforcement learning

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

Cross-media semantic retrieval (CSR) and cross-modal semantic mapping are key problems of the multimedia search engine. The cognitive function and neural structure for visual and auditory information process are an important reference for the study of brain-inspired CSR. In this paper, we analyze the hierarchy, the functionality and the structure of visual and auditory in the brain. Considering an idea from deep belief network and hierarchical temporal memory, we presented a brain-inspired intelligent model, called cross-media semantic retrieval based on neural computing of visual and auditory sensation (CSRNCVA). Algorithms based on CSRNCVA were developed. It employs belief propagation algorithms of probabilistic graphical model and hierarchical learning. The experiments show that our model and algorithms can be effectively applied to the CSR. This work provides an important significance for brain-inspired cross-media intelligence framework.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2018 Volume:28 | Number:4

Harvested from:

CDK

Metadata only:

false