Image segmentation method based on superpixels and immune sparse spectral clustering
An image segmentation and super-pixel technology, applied in the field of image processing, can solve the problems of low segmentation accuracy and low computational complexity, and achieve the effects of reducing the amount of calculation, improving segmentation accuracy, and improving accuracy
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[0024] The present invention will be described in further detail below in conjunction with the accompanying drawings.
[0025] Step 1, input the image to be segmented.
[0026] Select a texture image whose size is A=256*256 from the database as the input image to be segmented.
[0027] Step 2, the image to be segmented is divided into superpixels.
[0028] Existing methods for dividing superpixels include methods based on graph theory, methods based on entropy rate, method Meanshift based on gradient descent, simple linear iterative clustering method SLIC and level set method Turbopixels based on geometric flow, etc. In this example, the simple linear iterative clustering method SLIC is used to divide the image to be segmented into n superpixels. The implementation steps are as follows:
[0029] (2a) Evenly select n pixels in the image to be segmented as initial seed points, assign a label to each initial seed point, each seed point contains A / n pixel points, and the distanc...
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