Year of Award

2014

Degree Type

Thesis

Degree Name

Doctor of Philosophy (PhD)

Department

Department of Computer Science.

Principal Supervisor

Leung, Clement H. C.

Keywords

Digital techniques, Image analysis, Image processing, Information storage and retrieval systems

Language

English

Abstract

A flourishing World Wide Web dramatically increases the amount of images up­loaded and shared, and exploring them is an interesting and challenging task. While content-based image retrieval, which is based on the low level features extracted from images, has grown relatively mature, human users are more interested in the seman­tic concepts behind or inside the images. Search that is based solely on the low level features would not be able to satisfy users requirements and not e.ective enough. In order to measure the semantic similarity among images and increase the accuracy of Web image retrieval, it is necessary to dig the deep concept and semantic meaning of the image as well as to overcome the semantic gap. By exploiting the context of Web images, knowledge base and ontology-based similarities, through the analysis of user behavior of image similarity evaluation, we established a set of formulas which allows e.cient and accurate semantic similarity measurement of images. When jointly applied with ontology-based query expansion approaches and an adaptive image search engine for deep knowledge indexing, they are able to produce a new level of meaningful automatic image annotation, from which semantic image search may be performed. Besides, the semantic concept can be automatically enriched in MPEG-7 Structured Image Annotation approach. The system is evaluated quantitatively using more than thousands of Web images with associated human tags with user subjective test. Experimental results indicate that this approach is able to deliver highly competent performance, attaining good precision e.ciency. This approach enables an advanced degree of semantic richness to be automatically associated with images and e.cient image concept similarity measurement which could previously only be performed manually. Keywords: Image Index, Image Retrieval, Semantic Similarity, Relevance Feed­back, Knowledge Base, Ontology, Query Expansion, MPEG-7 . . .

Comments

Thesis (Ph.D.)--Hong Kong Baptist University, 2014.;Principal supervisor: Prof. Leung Clement H. C.;Includes bibliographical references (pages 177-206)

Large files may be slow to open. For best results, right-click and select "save as..."


Share

COinS
 
 

To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.