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Improving clustering-based image seg...

Zhang, Hui.

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Improving clustering-based image segmentation through learning.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Improving clustering-based image segmentation through learning./
Author:	Zhang, Hui.
Description:	138 p.
Notes:	Adviser: Sally Goldman.
Contained By:	Dissertation Abstracts International68-09B.
Subject:	Computer Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3282458
ISBN:	9780549249832

Improving clustering-based image segmentation through learning.
Zhang, Hui.

Improving clustering-based image segmentation through learning. - 138 p.

Adviser: Sally Goldman.

Thesis (Ph.D.)--Washington University in St. Louis, 2007.

Semantic image segmentation aims to partition an image into separate regions, which ideally correspond to different real-world objects. It is one of the most critical steps towards content analysis and image understanding. Image segmentation can be viewed as a clustering problem attempting to determine which pixels belong together. Although many different clustering techniques have been applied in the attempt to achieve a better segmentation method, most of them use only simple similarity measures such as the distance between image features. We give a justification that segmentation methods using only simple similarity measures are inherently biased. In this dissertation, we aim to improve clustering-based segmentation using evaluation measures, which incorporate prior knowledge about what segmentations are more preferred. We propose two machine learning-based ensemble evaluation techniques: Co-Evaluation and Meta-Evaluation, to improve evaluation accuracy. And we extend ensemble evaluation to the collective supervised clustering (CSC) framework, which enables a segmentation algorithm to use a set of similarity measures or evaluation measures collaboratively in the clustering process. By assigning different weights to different similarity/evaluation measures according to the characteristics of the measures and the content of the image to be segmented, better segmentation results can be achieved using CSC. Clustering-based segmentation methods using prior knowledge about object(s), either by giving object templates or by learning, are also discussed in this dissertation.

ISBN: 9780549249832Subjects--Topical Terms:

626642
Computer Science.

Improving clustering-based image segmentation through learning.
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245 1 0 $a Improving clustering-based image segmentation through learning.
300 $a 138 p.
500 $a Adviser: Sally Goldman.
500 $a Source: Dissertation Abstracts International, Volume: 68-09, Section: B, page: 6099.
502 $a Thesis (Ph.D.)--Washington University in St. Louis, 2007.
520 $a Semantic image segmentation aims to partition an image into separate regions, which ideally correspond to different real-world objects. It is one of the most critical steps towards content analysis and image understanding. Image segmentation can be viewed as a clustering problem attempting to determine which pixels belong together. Although many different clustering techniques have been applied in the attempt to achieve a better segmentation method, most of them use only simple similarity measures such as the distance between image features. We give a justification that segmentation methods using only simple similarity measures are inherently biased. In this dissertation, we aim to improve clustering-based segmentation using evaluation measures, which incorporate prior knowledge about what segmentations are more preferred. We propose two machine learning-based ensemble evaluation techniques: Co-Evaluation and Meta-Evaluation, to improve evaluation accuracy. And we extend ensemble evaluation to the collective supervised clustering (CSC) framework, which enables a segmentation algorithm to use a set of similarity measures or evaluation measures collaboratively in the clustering process. By assigning different weights to different similarity/evaluation measures according to the characteristics of the measures and the content of the image to be segmented, better segmentation results can be achieved using CSC. Clustering-based segmentation methods using prior knowledge about object(s), either by giving object templates or by learning, are also discussed in this dissertation.
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