Image retrieval method, device, computing device and medium based on small world network

A small-world network and image retrieval technology, applied in computing, computing models, computer components, etc., can solve problems that are prone to misjudgment, consume a lot of manpower and time, and achieve increased speed, reduced computing load, and improved processing The effect of efficiency and accuracy

Active Publication Date: 2019-03-01
GUANGZHOU HUIRUI SITONG INFORMATION SCI & TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0002] For a long time, the classification and search of evidence images by the public security, procuratorate and law departments have still remained at the stage of manual processing. However, due to the fact that most of the evidence images have text and the differences are small, when the amount of data in the image database is large, manual processing not only consumes a lot of manpower and time, and it is prone to misjudgment

Method used

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  • Image retrieval method, device, computing device and medium based on small world network
  • Image retrieval method, device, computing device and medium based on small world network
  • Image retrieval method, device, computing device and medium based on small world network

Examples

Experimental program
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Effect test

Embodiment 1

[0076] like figure 1 As shown, this embodiment provides an image retrieval method based on a small world network, and the method includes the following steps:

[0077] S101. Create an image library.

[0078] When creating an image library, a large number of images can be directly acquired and then created, but the image library to be created in this embodiment is an evidence image library, so the images to be acquired are evidence images, and the evidence images are converted through evidence files. The quality is the most important, and all the evidence materials need to be converted into a unified image format and named according to the content. Therefore, this step S101 is as follows: figure 2 shown, including:

[0079] S1011. Acquire multiple images.

[0080] The image obtained in this embodiment is an evidence image, which can be acquired through collection, for example, by uniformly scanning a paper evidence file into an electronic image format through a scanner, or ...

Embodiment 2

[0114] like Image 6 As shown, this embodiment provides an image retrieval device based on a small world network, the device includes an image library creation module 601, a first feature extraction module 602, a learning module 603, a small world network construction module 604, and a second feature extraction module 604. Module 605, feature matching module 606 and similarity judging module 607, the specific functions of each module are as follows:

[0115] The image library creation module 601 is used to create an image library.

[0116] Further, the image library creation module 601 is as follows Figure 7 shown, including:

[0117] The acquiring unit 6011 is used for acquiring multiple images.

[0118] The detection unit 6012 is configured to detect the resolution of the acquired image.

[0119] The resolution reducing unit 6013 is configured to reduce the resolution of the image when there is an image with a resolution greater than a predetermined resolution value to ...

Embodiment 3

[0136] This embodiment provides a computing device, and the computing device may be a computer, such as Figure 10 As shown, includes a processor 1002, memory, display 1003, and a network interface 1004 connected by a system bus 1001. The processor 1002 is used to provide computing and control capabilities, the memory includes a non-volatile storage medium 1005 and an internal memory 1006, the non-volatile storage medium 1005 stores an operating system, computer programs and databases, and the internal memory 1006 is The operating system and the computer program in the non-volatile storage medium provide an environment for running the computer program. When the computer program is executed by the processor 1002, the image retrieval method of the above-mentioned Embodiment 1 is realized, and the specific steps are: creating an image library; extracting each image in the image library The local features of the image; learn the local features of each image in the image library to...

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Abstract

The invention discloses an image retrieval method, a device, a computing device and a medium based on a small world network. The method includes creating an image base; extracting the local features of each image in the image library; learning the local features of each image in the image library to obtain a learning dictionary; according to the learning dictionary, the eigenvector of each image in the image library is generated, and the small world network of the eigenvector is constructed; extracting local features of the target image; matching the local features of the target image with thefeatures of the image in the small world network; according to the matching result, the images that satisfy the similarity threshold condition in the image library are selected and/or the images in the image library are sorted from high to low according to the similarity measure. A learn dictionary is obtained by machine learn, and a small-world network with image characteristics is constructed according to that learn dictionary, and the small-world network is used as an image retrieval model, thereby greatly improving the processing efficiency and accuracy of image retrieval and increasing the service throughput.

Description

technical field [0001] The invention relates to an image retrieval method, in particular to an image retrieval method, device, computing device and medium based on a small world network, and belongs to the field of image retrieval. Background technique [0002] For a long time, the classification and search of evidence images by the public security, procuratorial and legal departments have remained at the stage of manual processing. However, due to the fact that there are many texts in the evidence images and the differences are small, when the amount of data in the image database is large, manual processing not only consumes a lot of manpower and time, and it is prone to misjudgment. [0003] With the development of artificial intelligence technology and the improvement of hardware computing power, machine learning algorithms have been widely used in the field of image processing. Evidence images are usually scanned from documents, with mostly text and noise and offset. At ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/583G06K9/62G06N20/00
CPCG06F18/28
Inventor 卢溜
Owner GUANGZHOU HUIRUI SITONG INFORMATION SCI & TECH CO LTD
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