Image semantic segmentation method adopting inverse attention and pixel similarity learning

Abstract

The present invention provides an image semantic segmentation method adopting inverse attention learning and pixel similarity learning. The method comprises the following steps that: step A, preliminary semantic segmentation is performed on input images, and the branch networks of different scales in a DeepLab v2 ResneT1 101 network are adopted to extract the features of the input images of different scales; step B, on the basis of the step A, an inverse attention layer is adopted to segment the boundaries of the input images; step C, on the basis of the step A, a pixel similarity learning layer is adopted to further segment the boundaries of the input images; step D, the inverse attention layer and the pixel similarity learning layer are optimized, and a corresponding loss function is defined; and step E, network parameters are trained. According to the image semantic segmentation method of the present invention, the inverse attention mechanism is adopted to correct boundary locatingbetween a target area and a background area; the pixel similarity learning mechanism is adopted to solve the problems of boundary locating ambiguity and boundary smoothing between the target area andthe background area; and therefore, the effective segmentation of a fused area between the target area and the background area can be realized.

Description

technical field [0001] The invention relates to the field of computer vision, in particular to an image semantic segmentation method using inverse attention and pixel similarity learning. Background technique [0002] Image semantic segmentation technology is one of the hot and difficult issues in the field of computer vision. This technology divides the image into several different semantic regions, and recognizes the category of each region to obtain the final semantic annotation. Image semantic segmentation technology is automatically Driving, positioning and navigation based on image semantics, medical image analysis and other fields have a wide range of application values. [0003] According to the different semantic feature extraction, image semantic segmentation is usually divided into two categories: traditional and non-traditional methods; traditional image semantic segmentation methods include threshold-based image semantic segmentation, region-based image semantic...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide an image semantic segmentation method using inverse attention and pixel similarity learning, which solves the image segmentation problem of complex image scenes

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