Rapid multi-modal image synergy segmentation method with unrelated scale feature

Abstract

The invention discloses a rapid multi-modal image synergy segmentation method with scale unrelated feature. The method includes the following steps: firstly, opening inputted image set files, extracting superpixel in sequence by the image in the current input image set by utilizing a subscriber line interface circuit (SLIC), and extracting the superpizel of each image; calculating color characteristics of the superpizel and the regional covariance matrix, and initializing the foreground region and the background region of the image; secondly, building superpixel multi-modal features and models of the foreground and background regions; thirdly, optimizing and saluting. The rapid multi-modal image synergy segmentation method with the unrelated scale feature has the advantages that the multi-modal features are further introduced into an energy equation of the image synergy segmentation. The higher accuracy rate is guaranteed and the operating speed of the algorithm is improved. In addition, scenes which are capable of being processed by the image synergy segmentation are greatly expanded due to the introduction of the multi-modal features, and the method has certain robustness over the complicated image background.

Description

technical field [0001] The invention belongs to the field of image processing and image analysis, and in particular relates to a fast and practical multi-modal image collaborative segmentation technology with scale-independent characteristics, which can be used to simultaneously segment similar image regions in an image group. Background technique [0002] Image co-segmentation technology is based on the assumption that the foreground contained in each of the two or more images has a similar color histogram, and uses an unsupervised algorithm for two or more images containing similar foreground or background. Or a supervised algorithm with a small amount of human-computer interaction, and an image segmentation method that obtains accurate segmentation of foreground and background. Most of the existing image collaborative segmentation methods are mostly performed at the pixel level. As the image size increases, the time and space complexity of the algorithm will increase expo...

AI Technical Summary

Problems solved by technology

Most of the existing image collaborative segmentation methods are mostly performed at the pixel level. As the image size increases, the time and space complexity of the algorithm will increase exponentially. This limitation obviously makes the pixel-level collaborative segmentation algorithm unable to handle high-resolution images. Image segmentation of high-speed images, and it is impossible to process a large number of images at the same time

[0003] In addition, the image collaborative segmentation method based on the random field model is still in the preliminary stage based on encouraging the consistency of the foreground area. Some features used in image segmentation, such as shape information, boundary information, etc., have not been applied in collaborative segmentation.

Although multimodal features have been widely used in the field of computer vision, image processing, and pattern recognition, their application in image collaborative segmentation is still in its infancy, lacking a unified multi-feature fusion and measurement method, and its application potential has not been reflected.

Images