.png){kind=icon}
返回
Object-based image labeling through learning by example and multi-level segmentation
.png){kind=icon}
PRE
AI
DOI：10.1016/S0031-3203(02)00250-9.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We propose a method for automatic extraction and labeling of semantically meaningful image objects using learning by example and threshold-free multi-level image segmentation. The proposed method scans through images, each of which is pre-segmented into a hierarchical uniformity tree, to seek and label objects that are similar to an example object presented by the user. By representing images with stacks of multi-level segmentation maps, objects can be extracted in the segmentation map level with adequate detail. Experiments have shown that the proposed multi-level image segmentation results in significant reduction in computation complexity for object extraction and labeling (compared to a single fine-level segmentation) by avoiding unnecessary tests of combinations in finer levels. The multi-level segmentation-based approach also achieves better accuracy in detection and labeling of small objects. (C) 2003 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
Keyword:
object-based image labeling
multi-level segmentation
hierarchical content description
learning by example
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。
