Semantic segmentation of weakly supervised images based on iterative mining of common features of objects

Abstract

The invention provides a weak supervised image semantic segmentation method based on iterative mining common features of objects, belonging to the technical field of pattern recognition. In the training phase, the method acquires the training data set, constructs and trains the multi-label classification network, and acquires the initial seed area corresponding to each training image. Then, the superpixel region and the region label of each training image are obtained for training the region classification network, and the updated region label of the superpixel region is obtained for trainingthe semantic segmentation network. After iteration, when the performance of the semantic segmentation network converges, the trained semantic segmentation network is obtained. In the use stage, the color image is input into the trained semantic segmentation network, and the network outputs the semantic segmentation results of the image. The invention can realize reliable pixel-level semantic segmentation under the condition of only image class label, reduces the time and labor cost of data labeling, and has wide application prospect.

Description

technical field [0001] The invention belongs to the technical field of pattern recognition, and in particular relates to a weakly supervised image semantic segmentation method based on iterative mining of common features of objects. Background technique [0002] Image semantic segmentation is an important research direction in pattern recognition and computer vision. It refers to the full understanding of image content through pixel-level recognition and segmentation of images. Therefore, it has a very broad application prospect in the fields of automatic driving and robot vision. . [0003] In intelligent application scenarios such as autonomous driving, the on-board computer needs to complete the perception of the scene first, and make corresponding movements and decisions based on the perceived information. Image semantic segmentation is an important part of visual scene perception. Image semantic segmentation realizes the computer's understanding of the pixel level of t...

AI Technical Summary

Problems solved by technology

[0006] However, under the condition that only image category labels are used as supervision information, the difficulty of designing and training neural networks in the existing weakly supervised image semantic segmentation is greatly increased.

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