Target prospect collaborative segmentation method combining significant detection and discriminant study

Abstract

The invention discloses a target prospect collaborative segmentation method combing significant detection and discriminant study. The method comprises the steps as follows: step one, each image in an image set is divided into a plurality of superpixel blocks, and characteristics of each superpixel block are extracted; step two, an image concentrated and shared significant area in the image set is extracted to serve as a target prospect, a non-significant area and an area which has significance but is not the image concentrated and shared area are taken as a background area, low-rank matrix decomposition is adopted to perform significant detection on the images, and logistic regression is adopted to select the shared significant area as a final target. According to the target prospect collaborative segmentation method combing significant detection and discriminant study, the significant area can be effectively detected by means of the low-rank matrix decomposition, the influence of background consistency is removed, and by means of the discriminant study, the shared and significant area can be extracted; the low-rank matrix decomposition and the discriminant study process are combined and optimized under the unified framework, are mutually influenced and are commonly promoted; and finally, the shared and significant area can be obtained to serve as the target prospect area.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a target foreground collaborative segmentation method of joint saliency detection and discriminative learning. Background technique [0002] Image object segmentation is a basic task in computer vision. Image segmentation has a great impact on many computer vision tasks, such as image retrieval and image editing. However, it is difficult for existing unsupervised methods to accurately segment a single image, and the segmentation is driven by visual tasks. Therefore, the existing technology proposes an interactive segmentation method, which achieves good results for single image segmentation, but due to the huge labor cost based on interactive segmentation, it cannot be applied to large-scale network images. [0003] In order to solve the above problems, the prior art also proposes a collaborative segmentation method. Image object collaborative segmentation is a process ...

AI Technical Summary

Problems solved by technology

[0004] The basic assumption of the existing image collaborative segmentation algorithm is that the common area that appears in the image set at the same time is the target foreground area. This assumption has obvious problems, because the consistent background area will also be segmented as the target foreground area.

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