.png){kind=icon}
返回
Self-Adaptive Superpixels Based on Neural Network Models
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2020.3011712.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, the self-adaptive superpixels are generated based on a neural network model. Superpixels are clusters of pixels, which can simplify the expression of images. Superpixels are widely used in the field of video/image processing. However, existing algorithms are mainly based on hand-crafted features, which will lose the details of the images. We use the neural network model to extract the deep features of the pixels instead of the hand-crafted features. A predicted object area is obtained according to the results of the neural network models. Self-adaptive superpixels are generated by the clustering algorithm based on the deep features of the pixels and the predicted object area. Finer superpixels are generated in the object area, and coarser superpixels are generated in background area. The generated self-adaptive superpixels can represent the image in a concise way and improve the segmentation accuracy. Experimental results show that the proposed algorithm outperforms several state-of-the-art methods on the BSDS500 dataset.
Keyword:
Feature extraction
Image segmentation
Predictive models
Neural networks
Clustering algorithms
Image color analysis
Principal component analysis
Clustering
neural network models
superpixel segmentation
self-adaptive
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
.png){kind=icon}
分享收藏Economic benefit evaluation method for the micro-grid renewable energy system operation微网可再生能源系统运行经济效益评价方法
PREAI
分享收藏分享收藏Health Literacy – a review of research using the European Health Literacy Questionnaire (HLS-EU-Q16) in 2010-2018健康素养-2010-2018使用欧洲健康素养问卷 (HLS-EU-Q16) 进行的研究综述
OAAI
OAAI分享收藏
查看更多内容