Image significance target detection method based on maximum neighborhood and superpixel segmentation

A technology of superpixel segmentation and maximum neighborhood, which is applied in image enhancement, image analysis, image data processing, etc., can solve the problems of low detection accuracy, low saliency detection accuracy, and inability to remove non-target areas, etc., to improve Accuracy, the effect of reducing color interference

Active Publication Date: 2019-04-16
XIDIAN UNIV
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Problems solved by technology

This method can provide the most eigenvalues ​​for image sub-blocks, but its defect is that the saliency detection image is obtained by weighting the difference values ​​between different sub-blocks of the image, and the salient objects in the detection image are also retained. Non-target regions, resulting in lower final saliency detection accuracy
[0005] As another example, in the article "Saliency de...

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  • Image significance target detection method based on maximum neighborhood and superpixel segmentation
  • Image significance target detection method based on maximum neighborhood and superpixel segmentation
  • Image significance target detection method based on maximum neighborhood and superpixel segmentation

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[0031] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0032] refer to figure 1 , an image salient target detection method based on maximum neighborhood and superpixel segmentation, comprising the following steps:

[0033] Step 1) Perform superpixel segmentation on the image to be detected:

[0034] The image to be detected adopts the SLIC superpixel segmentation method. SLIC is the abbreviation of simple linear iterative clustering algorithm (simple linear iterative cluster). The SLIC algorithm takes into account the space and color distance between pixels and divides the image into multiple pixel points. Finally, K superpixel blocks are obtained and saved. After comparing the experimental results of multiple commonly used values ​​K=200, 250, 300, 400, and 500, the number of divisions K=200 for the best experimental effect is obtained. This embodiment adopts The image to be detected s...

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Abstract

The invention provides an image significance target detection method based on maximum neighborhood and superpixel segmentation, which is used for solving the technical problem of low image saliency target detection accuracy in the prior art. The method comprises the following implementation steps: 1, carrying out super-pixel segmentation on a to-be-detected image; 2, counting the occurrence frequency of each color in the to-be-detected image; 3, performing color substitution on the to-be-detected image; 4, preprocessing the image after color substitution; 5, calculating an initial saliency image of the to-be-detected image; 6, determining significance values of the K super-pixel blocks; and 7, obtaining and outputting a final saliency image. According to the invention, the accuracy of image saliency target detection is improved, the image saliency targets can be consistent and highlighted, and the method can be used for an image preprocessing process in the field of computer vision.

Description

technical field [0001] The invention belongs to the technical field of computer image processing, and relates to an image salient object detection method, in particular to an image salient object detection method based on maximum neighborhood and superpixel segmentation, which can be used in the image preprocessing process in the field of computer vision . Background technique [0002] When human beings observe an image, they usually only pay attention to a more salient part of the whole image. Therefore, when the computer simulates the human visual system, the simulation is mainly performed by detecting salient regions in the image. Image salient object detection can improve the performance of many computer vision and image processing algorithms, and can be used in research fields such as image segmentation, object recognition, and image retrieval. [0003] According to the detection principle, image saliency target detection can be divided into three categories: models b...

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Application Information

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IPC IPC(8): G06T7/00G06T7/11G06T7/136G06T7/187G06T7/90
CPCG06T7/0002G06T2207/10024G06T7/11G06T7/136G06T7/187G06T7/90
Inventor 李洁张航王颖王飞陈聪张敏
Owner XIDIAN UNIV
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