Semantic-based image retrieval for multi-word text queries

Catalyzed by the development of digital technologies, the amounts of digital images being produced, archived and transmitted are reaching enormous proportions. It is hence imperative to develop techniques that are able to index,and retrieve relevant images through user‘s information need. Image retr...

Full description

Saved in:

Bibliographic Details
Main Author:	Zand, Mohsen
Format:	Thesis
Language:	English
Published:	2015
Subjects:	Image processing Digital techniques
Online Access:	http://psasir.upm.edu.my/id/eprint/57134/1/FSKTM%202015%2019RR.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-upm-ir.57134
record_format	uketd_dc
spelling	my-upm-ir.571342017-08-23T08:18:58Z Semantic-based image retrieval for multi-word text queries 2015-12 Zand, Mohsen Catalyzed by the development of digital technologies, the amounts of digital images being produced, archived and transmitted are reaching enormous proportions. It is hence imperative to develop techniques that are able to index,and retrieve relevant images through user‘s information need. Image retrieval based on semantic learning of the image content has become a promising strategy to deal with these aspects recently. With semantic-based image retrieval (SBIR), the real semantic meanings of images are discovered and used to retrieve relevant images to the user query. Thus, digital images are automatically labeled by a set of semantic keywords describing the image content. Similar to the text document retrieval, these keywords are then collectively used to index,organize and locate images of interest from a database. Nevertheless,understanding and discovering the semantics of a visual scene are high-level cognitive tasks and hard to automate, which provide challenging researchop portunities. Specifically, exploiting discriminatory features, handling the visual similarity between object classes and appearance diversity in each class,classification of low-level image visual features to appropriate semantic classes,comprehensively annotate images, and reliable indexing and ranking images through difficult queries are open issues to cope with. This study proposes newideas to overcome these challenges. First, a discriminatory image feature vector is generated using texture as a distinguishable visual feature. In the proposed method, the image texture which is extracted by the Gabor wavelet and the curvelet transforms in the spectral domain is encoded into polynomial coefficients. It not only provides rotation invariant features but also generates texture feature vectors with the maximum power of discrimination. Second, a context-aware and semantic-consistent image descriptor is presented to exploit the image visual attributes in a contextual space. The high-level visual space is constructed by a Dirichlet process regardless of the semantic classes, and then, the posteriors are used to build the contextual space. Image processing Digital techniques 2015-12 Thesis http://psasir.upm.edu.my/id/eprint/57134/ http://psasir.upm.edu.my/id/eprint/57134/1/FSKTM%202015%2019RR.pdf application/pdf en public phd doctoral Universiti Putra Malaysia Image processing Digital techniques
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English
topic	Image processing Digital techniques
spellingShingle	Image processing Digital techniques Zand, Mohsen Semantic-based image retrieval for multi-word text queries
description	Catalyzed by the development of digital technologies, the amounts of digital images being produced, archived and transmitted are reaching enormous proportions. It is hence imperative to develop techniques that are able to index,and retrieve relevant images through user‘s information need. Image retrieval based on semantic learning of the image content has become a promising strategy to deal with these aspects recently. With semantic-based image retrieval (SBIR), the real semantic meanings of images are discovered and used to retrieve relevant images to the user query. Thus, digital images are automatically labeled by a set of semantic keywords describing the image content. Similar to the text document retrieval, these keywords are then collectively used to index,organize and locate images of interest from a database. Nevertheless,understanding and discovering the semantics of a visual scene are high-level cognitive tasks and hard to automate, which provide challenging researchop portunities. Specifically, exploiting discriminatory features, handling the visual similarity between object classes and appearance diversity in each class,classification of low-level image visual features to appropriate semantic classes,comprehensively annotate images, and reliable indexing and ranking images through difficult queries are open issues to cope with. This study proposes newideas to overcome these challenges. First, a discriminatory image feature vector is generated using texture as a distinguishable visual feature. In the proposed method, the image texture which is extracted by the Gabor wavelet and the curvelet transforms in the spectral domain is encoded into polynomial coefficients. It not only provides rotation invariant features but also generates texture feature vectors with the maximum power of discrimination. Second, a context-aware and semantic-consistent image descriptor is presented to exploit the image visual attributes in a contextual space. The high-level visual space is constructed by a Dirichlet process regardless of the semantic classes, and then, the posteriors are used to build the contextual space.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Zand, Mohsen
author_facet	Zand, Mohsen
author_sort	Zand, Mohsen
title	Semantic-based image retrieval for multi-word text queries
title_short	Semantic-based image retrieval for multi-word text queries
title_full	Semantic-based image retrieval for multi-word text queries
title_fullStr	Semantic-based image retrieval for multi-word text queries
title_full_unstemmed	Semantic-based image retrieval for multi-word text queries
title_sort	semantic-based image retrieval for multi-word text queries
granting_institution	Universiti Putra Malaysia
publishDate	2015
url	http://psasir.upm.edu.my/id/eprint/57134/1/FSKTM%202015%2019RR.pdf
_version_	1747812167802945536

Semantic-based image retrieval for multi-word text queries

Similar Items