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Internal Generative Mechanism Based Otsu Multilevel Thresholding Segmentation for Medical Brain Images Read chapter Read first chapter

2015 | OriginalPaper | Chapter

Progressive Image Segmentation Using Online Learning

Authors : Jiagao Hu, Zhengxing Sun, Kewei Yang, Yiwen Chen

Published in: Advances in Multimedia Information Processing -- PCM 2015

Publisher: Springer International Publishing

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Abstract

This article proposed a progressive image segmentation, which allow users to segment images according to their preferences without any boring pre-labeling or training stages. We use an online learning method to train/update the segmentation model progressively. User can scribble on the image to label initial samples or correct the false-labeled regions of the result. To efficiently integrate the interaction with the learning and updating process, a three-level representation of images is built. The proposed method has three advantages. Firstly, the segmentation model can be learned online along with user’s manipulation without any pre-labeling. Secondly, the diversity of segmentation accord with user’s preferences can be met flexibly, and the more use the more accurate the segmentation could be. Finally, the segmentation model can be updated online to meet the needs of users. The experimental results demonstrate these advantages.

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previous chapter On-line Sample Generation for In-air Written Chinese Character Recognition Based on Leap Motion Controller
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Metadata
Title
Progressive Image Segmentation Using Online Learning
Authors
Jiagao Hu
Zhengxing Sun
Kewei Yang
Yiwen Chen
Copyright Year
2015
Book
Advances in Multimedia Information Processing -- PCM 2015

Print ISBN: 978-3-319-24074-9

Electronic ISBN: 978-3-319-24075-6

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-24075-6_18