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A stochastic segmentation method for...

The University of North Carolina at Charlotte., Information Technology (PhD).

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A stochastic segmentation method for interesting region detection and image retrieval.

Record Type:	Electronic resources : Monograph/item
Title/Author:	A stochastic segmentation method for interesting region detection and image retrieval./
Author:	Li, Zhong.
Description:	166 p.
Notes:	Adviser: Jianping Fan.
Contained By:	Dissertation Abstracts International70-09B.
Subject:	Computer Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3372075
ISBN:	9781109343540

A stochastic segmentation method for interesting region detection and image retrieval.
Li, Zhong.

A stochastic segmentation method for interesting region detection and image retrieval. - 166 p.

Adviser: Jianping Fan.

Thesis (Ph.D.)--The University of North Carolina at Charlotte, 2009.

The explosively increasing digital photo urges for an efficient image retrieval system so that digital images can be organized, shared, and reused. Current content based image retrieval (CBIR) systems face multiple challenges in all aspects: image representation, classification and indexing. Image representation of current CBIR system is of such low quality that the background is often mixed with the objects which makes the signature of an image less distinguishable or even misleading. An image classifier connects the low level feature with the high level concept and the low quality feature will only make the effort of bridging of the semantic gap harder.

ISBN: 9781109343540Subjects--Topical Terms:

626642
Computer Science.

A stochastic segmentation method for interesting region detection and image retrieval.
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020 $a 9781109343540
035 $a (UMI)AAI3372075
035 $a AAI3372075
040 $a UMI $c UMI
100 1 $a Li, Zhong. $3 1059017
245 1 2 $a A stochastic segmentation method for interesting region detection and image retrieval.
300 $a 166 p.
500 $a Adviser: Jianping Fan.
500 $a Source: Dissertation Abstracts International, Volume: 70-09, Section: B, page: .
502 $a Thesis (Ph.D.)--The University of North Carolina at Charlotte, 2009.
520 $a The explosively increasing digital photo urges for an efficient image retrieval system so that digital images can be organized, shared, and reused. Current content based image retrieval (CBIR) systems face multiple challenges in all aspects: image representation, classification and indexing. Image representation of current CBIR system is of such low quality that the background is often mixed with the objects which makes the signature of an image less distinguishable or even misleading. An image classifier connects the low level feature with the high level concept and the low quality feature will only make the effort of bridging of the semantic gap harder.
520 $a A new system to tackle these challenges more efficiently has been developed. My contribution consists of: (a) A stochastic image segmentation algorithm that is able to achieve better balance on integrity/oversegmentation. The algorithm estimates the average contour conformation and obtains more accurate results and is very attractive for feature extraction for customer photos as well as for tissue segmentation in 3D medical images. (b) A new interesting region detection method which can seamlessly integrate GMM and SVM in one scheme. It proves that the pattern of the common interests can be efficiently learned using the interesting region classifier. (c) The popularity and useability of the metadata of the +200 different models sold on market is explored and metadata is used both for interesting region detection and image classification. This incorporation of camera metadata has been missed in the computer vision community for decades. (d) A new high dimensional GMM estimator that tackles the oscillation of principle dimensionality of GMM in high dimension in real world dataset by estimating the average conformation along the evolution history. (e) An image retrieval system that can support query by keyword, query by example, and ontology browsing alternatively.
590 $a School code: 0694.
650 4 $a Computer Science. $3 626642
650 4 $a Information Science. $3 1017528
690 $a 0723
690 $a 0984
710 2 $a The University of North Carolina at Charlotte. $b Information Technology (PhD). $3 1059016
773 0 $t Dissertation Abstracts International $g 70-09B.
790 $a 0694
790 1 0 $a Binkley, David M. $e committee member
790 1 0 $a Dai, Xingde $e committee member
790 1 0 $a Fan, Jianping, $e advisor
790 1 0 $a Lu, Aidong $e committee member
790 1 0 $a Subramanian, Kalpathi $e committee member
791 $a Ph.D.
792 $a 2009
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3372075