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Image-text double-coding mechanism implementation model based on CR<2> neural network

A neural network and dual-coding technology, applied in the field of human cognition and knowledge representation, can solve problems such as the difficulty of establishing mathematical models

Inactive Publication Date: 2017-08-04
CHONGQING UNIV
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Problems solved by technology

Double coding theory is an important theory in cognitive science, but it only stays in the theoretical text expression, and the establishment of a complete mathematical model is still a difficult point

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  • Image-text double-coding mechanism implementation model based on CR&lt;2&gt; neural network
  • Image-text double-coding mechanism implementation model based on CR&lt;2&gt; neural network

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Embodiment Construction

[0023] The method described in the present invention will be further described in detail below in conjunction with the accompanying drawings. figure 1 The system structure block diagram of the image-text double encoding mechanism realization model provided by the present invention; figure 2 The schematic diagram of the system principle of the image-text double encoding mechanism realization model provided by the present invention is shown in the figure: the image-text double encoding mechanism realization model provided by the present invention includes the following steps:

[0024] S1: Input image information and text information related to the information;

[0025] S2: Obtain the "image unit" of the non-verbal representation of the information through the representation system;

[0026] S3: Obtain the "linguistic unit" represented by the information word through the semantic system;

[0027] S4: Obtain the associated "image unit" and its "language unit" by referring to th...

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Abstract

The invention discloses an image-text double-coding mechanism implementation model based on a CR2 neural network, and relates to the field of human cognition and knowledge representation. The image-text double-coding mechanism implementation model based on the CR<2> neural network is characterized in that the implementation model includes a representation system which adopts a multilayer Convolutional Neural Network (CNN) model to obtain ''image units'' representing mental images of information; and a semantic system which adopts an RNNLM language model to obtain ''language units'' representing information semantics; a reference association system which adopts an RBF self-propagation neural network model, wherein a positive model uses the ''image units'' as input, output is the ''language units'' associated for reference, and an inverse model is inverse operation of the positive model. Here the CR<2> neural network refers to an organic composition of three neural networks of CNN, RNN and RBF. The image-text double-coding mechanism implementation model based on the CR2 neural network realizes establishment of models of an image representation system and a natural language semantic system, and also establishes a model of reference association between the two systems. The models completely simulate the whole process of an image-text double-coding cognition mechanism.

Description

technical field [0001] The invention relates to the field of human cognition and knowledge representation, in particular to an image-text double-coding cognitive mechanism model based on a multilayer neural network and a method for establishing the same. Background technique [0002] In recent years, research on human cognition and knowledge representation has become a hot topic in the scientific community; at the same time, this is also a key point in artificial intelligence research. Dual coding is a cognitive theory proposed by psychologist Pevio in 1971, which emphasizes that language and non-language information processing are equally important in the storage, processing and retrieval of information. There are two subsystems in human cognition, one is dedicated to the representation and processing of non-verbal things and events (ie images), the representational system; the other is used for language processing, the semantic system. These two subsystems are parallel to...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/04
CPCG06N3/0463G06N3/045
Inventor 李军陈剑斌沈广田高杨建许阳
Owner CHONGQING UNIV
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