Method for image classification and brain-imitated storage based on user group optimization

A user group, distributed storage technology, applied in the direction of still image data retrieval, still image data index, metadata still image retrieval, etc. To meet the needs of users and other issues, to achieve the effect of optimizing storage scheduling and resource utilization, improving efficiency and quality

Active Publication Date: 2017-12-22
SHANGHAI UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Existing network sharing models cannot fundamentally eliminate the problem of "information islands"
[0004] In addition, the current traditional image classification and storage methods are all single-mode, in other words, the storage system is mainly responsible for access, while the classification system is only responsible for labeling the images, and finally the classification information is sent back to the storage system
This method is inconvenient for users to operate
Therefore, when the image required by the user is incorrectly classified by the system, the system will not be able to return the correct category, making the tradition

Method used

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  • Method for image classification and brain-imitated storage based on user group optimization
  • Method for image classification and brain-imitated storage based on user group optimization
  • Method for image classification and brain-imitated storage based on user group optimization

Examples

Experimental program
Comparison scheme
Effect test

Example Embodiment

[0021] Example one:

[0022] See figure 1 , This brain-like storage method for image classification based on user group optimization, the operation steps are as follows:

[0023] a) Establish an image data representation model: a basic model used to represent an image.

[0024] b) Build a distributed brain-like storage system: realize distributed brain-like storage of image files.

[0025] c) Automatic image annotation and classification: A method for automatic image annotation and classification is proposed.

[0026] d) Carry out annotation correction: improve the accuracy of annotated images through manual intervention.

Example Embodiment

[0027] Embodiment two:

[0028] This embodiment is basically the same as the first embodiment, and the special features are as follows:

[0029] See figure 2 , The method of establishing the image data representation model in the above step a) is as follows:

[0030] The image data model uses fuzzy mathematics to use five fuzzy sets to represent the attributes of each layer of an image while ensuring the uniqueness of these images:

[0031] a) The original data of the image;

[0032] b) The basic attributes of the image (including file name, image format, creation time, etc.);

[0033] c) The low-level features of the image (including color, texture, shape, etc.);

[0034] d) The semantic features of the image (including subjective interpretation of the image, understanding of low-level features, etc.);

[0035] e) The relationship between image low-level features and semantic features.

[0036] The specific description is as follows:

[0037] Image data representation model G:

[0038] G=(D...

Example Embodiment

[0049] Embodiment three:

[0050] This embodiment is basically the same as the first embodiment, and the special features are as follows:

[0051] See image 3 , The brain-like distributed storage method in step b) above is:

[0052] According to the characteristics of human brain data storage, a similar image distributed storage system is constructed. Store image data with similar attributes in physically adjacent locations. This facilitates Lenovo access and regional access, improves the efficiency and quality of access, and optimizes storage scheduling and resource utilization.

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Abstract

The invention provides a method for classified storage of distributed image documents based on a brain storage mode. The method comprises the operation steps that (1) an image data expression model is established; (2) a brain-imitated distributed storage system is established; (3) images are labeled and classified automatically; and (4) label amendment is conducted. With the method, automatic labeling results of the images can be obtained through automatic labeling; and then the label amendment can be conducted based on a user's understanding of pictures. The method can also feed back similar images according to the user's demands for image types. The user can obtain classification results of the designated images and similar pictures. The user can conduct artificial intervention and amendment of the obtained classification results, so as to improve accuracy of the image classification.

Description

technical field [0001] The present invention relates to the field of computer artificial intelligence, in particular to an image classification brain-like storage method based on user group optimization, which is a brain-like distributed storage of images and an image classification storage method combining picture features with traditional classifiers . Background technique [0002] In the current information age, the growth rate of data scale far exceeds our expectations, and there are a large number of picture files. In this case, the centralized file system cannot effectively manage these image files. The traditional approach to image management is text-based retrieval. Although this method is easy to implement, it is difficult for text description images to fully express rich content. In addition, manual annotation is time-consuming and laborious. There are many kinds of images, and there are also many ways to store different image types. There is a data structure a...

Claims

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Application Information

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IPC IPC(8): G06F17/30G06K9/62
CPCG06F16/13G06F16/16G06F16/172G06F16/182G06F16/51G06F16/58G06F18/2411
Inventor 武星
Owner SHANGHAI UNIV
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