分割
邻接表
人工智能
公制(单位)
计算机科学
计算机视觉
对象(语法)
图像分割
边界(拓扑)
编码器
尺度空间分割
基于分割的对象分类
模式识别(心理学)
数学
算法
操作系统
数学分析
经济
运营管理
作者
Dazhou Guo,Ligeng Zhu,Yuhang Lu,Hongkai Yu,Song Wang
出处
期刊:IEEE transactions on image processing
[Institute of Electrical and Electronics Engineers]
日期:2019-06-01
卷期号:28 (6): 2643-2653
被引量:33
标识
DOI:10.1109/tip.2018.2888701
摘要
Recent advancements in deep learning have shown an exciting promise in the urban street scene segmentation. However, many objects, such as poles and sign symbols, are relatively small, and they usually cannot be accurately segmented, since the larger objects usually contribute more to the segmentation loss. In this paper, we propose a new boundary-based metric that measures the level of spatial adjacency between each pair of object classes and find that this metric is robust against object size-induced biases. We develop a new method to enforce this metric into the segmentation loss. We propose a network, which starts with a segmentation network, followed by a new encoder to compute the proposed boundary-based metric, and then trains this network in an end-to-end fashion. In deployment, we only use the trained segmentation network, without the encoder, to segment new unseen images. Experimentally, we evaluate the proposed method using CamVid and CityScapes data sets and achieve a favorable overall performance improvement and a substantial improvement in segmenting small objects.
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