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Weakly-Supervised Image Semantic Segmentation Method Based on Candidate Regions and Neighborhood Classifiers

A candidate region and semantic segmentation technology, applied in the field of computer vision, can solve the problem of insufficient semantic label inference, and achieve the effect of ensuring accuracy, improving accuracy and improving accuracy.

Active Publication Date: 2022-06-21
TAIYUAN UNIVERSITY OF SCIENCE AND TECHNOLOGY
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  • Application Information

AI Technical Summary

Problems solved by technology

However, the classification network can only identify some small discriminative object regions, and cannot sufficiently realize the inference of semantic labels.

Method used

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  • Weakly-Supervised Image Semantic Segmentation Method Based on Candidate Regions and Neighborhood Classifiers
  • Weakly-Supervised Image Semantic Segmentation Method Based on Candidate Regions and Neighborhood Classifiers
  • Weakly-Supervised Image Semantic Segmentation Method Based on Candidate Regions and Neighborhood Classifiers

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Embodiment Construction

[0037] figure 1 The overall processing flow of the present invention is given, which is combined below figure 1 The present invention is further described.

[0038] The present invention provides a weakly supervised image semantic segmentation method based on high-precision candidate regions and neighborhood classifiers. The main steps are described as follows:

[0039] Step 1. First, perform linear spectral clustering superpixel segmentation on the training image to obtain the desired superpixels, and then combine superpixels based on visual features until the number of combined superpixels is equal to the multiple of the number of labels contained in the image-level label, and obtain A collection of training images consisting of candidate regions.

[0040] Compared with superpixels as the basic processing unit, the number of candidate regions in the image is smaller, which is more helpful for improving the accuracy of semantic label inference. In addition, the candidate r...

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Abstract

The present invention relates to the field of computer vision, a weakly supervised image semantic segmentation method based on candidate regions and neighborhood classifiers, using candidate regions as the basic processing unit, and applying neighborhood classifiers to image semantic segmentation to obtain high-precision, less susceptible Weakly Supervised Image Semantic Segmentation Confused by Noisy Labels. As a key technology urgently needed in the current field of computer vision, the method of the invention can realize the label prediction of each pixel in the test image, and can obtain relatively high semantic segmentation accuracy of the weakly supervised image.

Description

technical field [0001] The invention relates to the field of computer vision, in particular to a weakly supervised image semantic segmentation method based on high-precision candidate regions and neighborhood classifiers. Background technique [0002] The application and demand of image semantic segmentation in the fields of autonomous driving, video surveillance, augmented reality, UAV application and medical image analysis are more and more extensive and urgent. Although fully supervised image semantic segmentation has made great progress in the fields of image classification and object recognition with the help of deep convolutional neural networks. However, fully supervised image semantic segmentation is a data-hungry task that requires a large amount of training data with pixel-level annotations. At the same time, annotating large amounts of pixel-level data is very time-consuming and labor-intensive. [0003] However, image-level weakly supervised annotations are not...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/11G06V10/26G06V10/56G06V10/74G06V10/762G06V10/764G06K9/62
CPCG06T7/11G06T2207/20081G06V10/267G06V10/56G06F18/23G06F18/22G06F18/241
Inventor 谢刚谢新林赵文晶郭磊王银
Owner TAIYUAN UNIVERSITY OF SCIENCE AND TECHNOLOGY
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