The invention discloses a domain adaptive semantic segmentation method based on similarity space alignment. The segmentation output of a source domain and the segmentation output of a target domain are respectively transformed into a similarity space, and the similarity space distribution of the source domain and the target domain is aligned to reduce the inter-domain difference, so that a semantic segmentation model with a better segmentation effect on an unsupervised target domain can be obtained. According to the method, the concept of similarity space is introduced into a cross-domain semantic segmentation task, the correlation between categories in a segmentation scene is better coded, and the discriminator is used for discriminating the similarity space of different domains, so thatthe segmentation network pays more attention to the structure, category coexistence and other information of an image, and the whole network can be trained end to end. The unsupervised domain self-adaptive semantic segmentation method based on similarity space alignment is innovated on the basis of the existing technical thought, the correlation space information of categories in a segmentation scene is fused, the segmentation performance is better, and the method has very high practical application value.

Domain adaptive semantic segmentation method based on similarity space alignment

The invention discloses an image segmentation level selection method based on scale perception, and aims to improve the segmentation quality of an overall image. The method comprises the following main steps: firstly, obtaining a segmentation result represented by a tree form by using a multi-level image segmentation algorithm; secondly, calculating feature vectors of all segmented regions in eachsegmentation layer, and performing quantitative description on region segmentation quality according to a visual format tower principle; thirdly, constructing a graph model of the multi-level segmented image by taking the hierarchical region with the finest segmentation granularity as a node, and finally, mapping a label to a region corresponding to the original hierarchy, and combining to obtaina final image segmentation result. According to the method, based on the scale selection principle of segmentation area quality, the problem of hierarchical selection of multi-level image segmentation is solved, and the limitation of traditional single hierarchical selection and the uncertainty of threshold parameters on segmentation hierarchical selection are overcome. As a post-processing means, the output quality of the multi-level image segmentation algorithm in a visual processing task can be improved.

Image segmentation level selection method based on scale perception

An image segmentation method based on multi-level region synthesis includes the steps of firstly, obtaining multi-level segmentation results by using an existing multi-level image segmentation algorithm; secondly, performing synthesis in a global level range: selecting multiple segmentation results of an image from a low level to a high level, respectively calculating image region characteristicsof each level, performing unified quantitative description of the multiple characteristics, establishing a synthesized model of multi-level image segmentation, and performing the optimal combination of the segmentation regions by using a multi-label graph cutting method; thirdly, according to the global-level synthesized results, selecting a local level range and performing a second synthesis by using a multi-label graph cutting model; and fourthly, subjecting the level labels by the second synthesis to regional mapping to obtain a final image segmentation result The image segmentation methodbased on the multi-level region synthesis in the invention selects the region with high target segmentation quality from the multiple segmentation levels and realizes adaptive selection; uses less regional features to calculate the segmentation quality and reduces the number of regional nodes involved in the calculation, with the used optimized combination model having a better optimization effect

Image segmentation method based on multi-level region synthesis

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Image segmentation method based on multi-level region synthesis