Weak supervised image semantic segmentation method based on superpixels and conditional random field
A conditional random field and semantic segmentation technology, applied in the field of image processing, can solve the problems of difficulty in obtaining pixel-level annotations for fully supervised image semantic segmentation, and low precision of weakly supervised image semantic segmentation, achieving accurate image semantic segmentation results and reducing complexity , Image segmentation accurate effect
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[0031] Attached below figure 1 Specific embodiments of the present invention are described in detail, a weakly supervised image semantic segmentation method based on superpixels and conditional random fields, the specific steps are as follows:
[0032] 1) Use the SLIC superpixel segmentation algorithm to segment the image
[0033] a) Suppose there are N (N is a natural number) pixels in the picture, the number of pre-segmented superpixels is K (the number of K is adaptively generated), the size of each superpixel is N / K, and the superpixel The closest distance to the center point is denoted as S, and the cluster center is initialized with a grid with a step size of S.
[0034] b) Adaptively generate the K value of the pre-segmented superpixel number. First convert the RGB image to HSV mode, l max is the maximum value of the three channels of R, G, and B, l min It is the minimum value of the three channels of R, G, and B. According to the formulas (1), (2), and (3), the RGB...
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