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Significance-based self-adaption threshold segmentation and extraction algorithm of natural object image

An adaptive threshold, target image technology, applied in the field of image processing, can solve problems affecting image synthesis and so on

Active Publication Date: 2017-01-04
XIAN UNIV OF TECH
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Problems solved by technology

[0006] (2) Natural image segmentation based on saliency can only extract a certain part of the object sometimes, and the result of natural image segmentation in the image database will directly affect the subsequent image synthesis

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  • Significance-based self-adaption threshold segmentation and extraction algorithm of natural object image

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Embodiment Construction

[0078] The present invention will be described in detail below in combination with specific embodiments.

[0079] The saliency-based self-adaptive threshold natural target image segmentation and extraction algorithm of the present invention comprises the following steps:

[0080] Step 1. Carry out cluster segmentation and extraction of salient information on the natural target image to obtain a mean salient image;

[0081] Step 1 includes the following steps:

[0082] Step 1.1, create an M*N image matrix im, store the image matrix im as an input natural image, convert the natural target image from the RGB color space to the CIELAB color space, and save it as the image matrix labim;

[0083] The process of converting the RGB color space to the CIELAB (L*a*b*) color space is as follows, first convert the RGB color space to the CIEXYZ color space according to the formula (1),

[0084] X Y ...

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Abstract

The invention discloses a significance-based self-adaption threshold segmentation and extraction algorithm of a natural object image. The algorithm comprises following steps of: step 1, carrying out clustering segmentation and significance information extraction on a natural target image so as to obtain an average value significant image; and step 2, according to the average value significant image, carrying out self-adaption threshold segmentation on the natural target image so as to obtain an unprocessed segmentation result binary image and carrying out region filling on the unprocessed segmentation result binary image so as to a complete target binaryzation image. According to the invention, by counting the color distribution information, the number of peak values is effectively calculated and then used as a cluster center number K of a K-means cluster, so effects of artificial factors on a segmentation process are eliminated. Meanwhile, compared with the Mean Shift algorithm, the algorithm provided by the invention is characterized in that the clustering results of the K-means algorithm are better. Therefore, the accuracy of the segmentation result is perfect.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a saliency-based self-adaptive threshold natural object image segmentation and extraction algorithm. Background technique [0002] With the development of computer vision and image processing technology, the generation of natural image scenes based on sketches has been widely used in children's teaching, animation games, animation and other fields. The key to realize this technology is the reasonable segmentation and extraction of sketch objects and natural target images, and the retrieval of natural target images based on sketches. Therefore, the key technology of sketch object and natural target image segmentation and extraction in the sketch-based natural image scene is studied. However, there is no segmentation algorithm model in the existing image segmentation methods that can meet the different needs of everyone for different images. . Therefore, rese...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06K9/62
CPCG06T2207/20004G06F18/23213
Inventor 石争浩郝欢金冬梅
Owner XIAN UNIV OF TECH
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