Similarity image retrieval method and system based on sparse coding

An image retrieval and sparse coding technology, applied in the field of image recognition, can solve the problems of relying on training samples, feature extraction methods are not very effective, and there is no targeted measurement standard, so as to achieve the effect of improving learning efficiency

Pending Publication Date: 2020-11-10
SHANDONG INST OF BUSINESS & TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] 1. The feature extraction method is not very effective and highly dependent on training sa

Method used

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  • Similarity image retrieval method and system based on sparse coding
  • Similarity image retrieval method and system based on sparse coding
  • Similarity image retrieval method and system based on sparse coding

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Experimental program
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Example Embodiment

[0028] Example 1

[0029] see figure 1 , figure 1 A schematic diagram of steps of a method for retrieving similarity images based on sparse coding provided by an embodiment of the present invention includes the following steps:

[0030] Step S100, performing basis vector characterization according to the reference image to obtain a first sparse characterization result;

[0031] Specifically, the reference image is the selected image to be retrieved, and the basis vector is the most basic component in the vector space, because the basis vector must be linearly independent, this part can represent each vector in the vector space, and reduce the image dimension, Obtain the first sparse representation result.

[0032] In some embodiments, the electronic device shoots in various scenarios, such as night scenes or backlit environments. Under the same shooting scene, the electronic device can shoot multiple frames of images, and perform image registration on the multiple frames o...

Example Embodiment

[0046] Example 2

[0047] see figure 2 , figure 2 A schematic diagram of detailed steps of a sparse coding-based similarity image retrieval method provided by an embodiment of the present invention, which includes the following steps:

[0048] Step S200, fixing the basis vector in the dictionary, and adjusting the coding coefficients so that the objective function is the smallest;

[0049] Specifically, by keeping the base vector in the dictionary unchanged and changing the coding coefficients, the objective function is minimized, thereby completing the dictionary training.

[0050] Step S210, fixing the coding coefficients, and adjusting the basis vectors in the dictionary so that the objective function is the smallest;

[0051] Specifically, by keeping the coding coefficient unchanged, the basis vector in the dictionary is changed to minimize the objective function, thereby completing the dictionary training.

[0052] Step S220, through continuous iteration until conve...

Example Embodiment

[0073] Example 3

[0074] see image 3 , image 3 This is a schematic diagram of modules of a similarity image retrieval system based on sparse coding provided by an embodiment of the present invention. A similarity image retrieval system based on sparse coding, which includes a first acquisition module for performing basis vector characterization according to a reference image to acquire a first sparse representation result, and a second acquisition module for performing basis vector characterization according to a reference image. The vector representation obtains the second sparse representation result, the calculation module is used to calculate the similarity between the first sparse representation result and the second sparse representation result, the judgment module is used to determine whether the similarity is greater than the preset threshold, and if so, it is determined as yes Similar images, if not, it is determined as a non-similar image.

[0075] It also incl...

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Abstract

The invention provides a similarity image retrieval method and system based on sparse coding, and relates to the field of image recognition. The similarity image retrieval method based on sparse coding comprises the following steps: performing base vector representation according to a reference image to obtain a first sparse representation result; performing base vector representation according tothe images in the image library to obtain a second sparse representation result; calculating the similarity between the first sparse representation result and the second sparse representation result;and judging whether the similarity is greater than a preset threshold, if yes, judging that the image is a similarity image, and if not, judging that the image is a non-similarity image. According tothe method, the image feature information can be extracted more fully, and the similarity calculation process is more accurate and targeted. In addition, the invention further provides a similarity image retrieval system based on sparse coding. The similarity image retrieval system comprises a first acquisition module, a second acquisition module, a calculation module and a judgment module.

Description

technical field [0001] The present invention relates to the field of image recognition, in particular to a similarity image retrieval method and system based on sparse coding. Background technique [0002] With the advent of the digital media era, massive digital images have become an indispensable part of our lives, and are widely used in life sciences, education, culture and other fields. Many classic machine learning methods, especially deep learning methods, can retrieve target images from massive image libraries. How to use a single image to retrieve semantically similar images from a massive image database has very good practical application value. [0003] However, traditional machine learning methods cannot be very effectively applied to the retrieval of single similar images. The current mainstream methods have the following problems: [0004] 1. The feature extraction method is not very effective and highly dependent on training samples; [0005] 2. There is no...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06K9/46
CPCG06V10/513G06V10/40G06V10/462G06F18/22G06F18/214
Inventor 华臻王浩然李小玲吴昊
Owner SHANDONG INST OF BUSINESS & TECH
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