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Image feature extraction method based on attention mechanism and convolutional neural network

A convolutional neural network and image feature extraction technology, applied in neural learning methods, biological neural network models, neural architectures, etc., can solve the problem of no separation of primary and secondary content of images, and achieve the effect of improving rationality

Inactive Publication Date: 2019-12-20
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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  • Abstract
  • Description
  • Claims
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Problems solved by technology

[0004] In view of the above-mentioned deficiencies in the prior art, the image feature extraction method based on the attention mechanism and the convolutional neural network provided by the present invention solves the problem that the existing image feature extraction results do not separate the primary and secondary content of the image

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  • Image feature extraction method based on attention mechanism and convolutional neural network
  • Image feature extraction method based on attention mechanism and convolutional neural network
  • Image feature extraction method based on attention mechanism and convolutional neural network

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Embodiment Construction

[0039] The specific embodiments of the present invention are described below so that those skilled in the art can understand the present invention, but it should be clear that the present invention is not limited to the scope of the specific embodiments. For those of ordinary skill in the art, as long as various changes Within the spirit and scope of the present invention defined and determined by the appended claims, these changes are obvious, and all inventions and creations using the concept of the present invention are included in the protection list.

[0040] like figure 1 As shown, the image feature extraction method based on attention mechanism and convolutional neural network includes the following steps:

[0041] S1. Input the original image into the encoder, and extract the corresponding feature vector;

[0042] S2. Select the extracted feature vectors through the attention mechanism strategy to determine the feature vectors of important image blocks;

[0043] S3. ...

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Abstract

The invention discloses an image feature extraction method based on an attention mechanism and a convolutional neural network. The method comprises: constructing a five-layer convolutional neural network model without a full connection layer for extracting image features; selecting image features through an attention mechanism strategy; organically combining an attention mechanism and a convolutional neural network, successfully extracting the most important image features corresponding to different decoding moments, so that accurate and higher-quality image features are provided for the subsequent decoding process, and the rationality of an image feature extraction result is improved to a certain extent.

Description

technical field [0001] The invention belongs to the technical field of image feature extraction, and in particular relates to an image feature extraction method based on an attention mechanism and a convolutional neural network. Background technique [0002] The role of image features is to describe image information, and image features in the physical sense generally include shape, color, texture, and spatial relationship. The shape of an image generally refers to the contour shape and the region shape. The contour shape represents the embodied edge shape, representing an external shape of the image as a whole, and the region feature represents the internal shape of the image. Color feature is a kind of global feature, which is the most obvious and most noticeable surface characteristic of the image, and the color feature is represented based on pixels. Like the color feature, the texture feature is also a global feature, which also represents the surface characteristics o...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/46G06N3/04G06N3/08
CPCG06N3/08G06V10/462G06N3/044G06N3/045
Inventor 李建平顾小丰胡健苌浩阳赖志龙张建国俞腾秋
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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