A STOCHASTIC SEGMENTATION METHOD FOR INTERESTING REGION DETECTION AND IMAGE RETRIEVAL

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Li, Z. (2009). A STOCHASTIC SEGMENTATION METHOD FOR INTERESTING REGION DETECTION AND IMAGE RETRIEVAL. Unc Charlotte Electronic Theses And Dissertations.
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Abstract

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.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/over-segmentation. 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.

Details
Author

Li, Zhong

Title

A STOCHASTIC SEGMENTATION METHOD FOR INTERESTING REGION DETECTION AND IMAGE RETRIEVAL

Physical Description

1 online resource (166 pages) : PDF

Date

2009

Degree Granting Institution

University of North Carolina at Charlotte

Abstract

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.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/over-segmentation. 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.

Genre

doctoral dissertations

Subjects--Topics

Computer science
Information science

Degree

Ph.D.

Keywords

Contour Confidence Map
Gaussian Mixture Model
Image Retrieval
Image Segmentation
Interesting Region Detection
Region Restricted Em Algorithm

Subject Area

Information Technology

Advisor(s)

Fan, Jianping

Committee Members

Lu, Aidong
Binkley, David
Subramanian, Kalpathi
Dai, Xingde

Degree Note

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

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Rights Holder Information

Copyright is held by the author unless otherwise indicated.

Identifier

Li_uncc_0694D_10026

Permalink

http://hdl.handle.net/20.500.13093/etd:1480