Image retrieval: modelling keywords via low-level features
Date Issued
May 2014
Author(s)
Advisor
Abstract
Although Content Based Image Retrieval (CBIR) has attracted large amount of research interest, the difficulties in querying by an example propel ultimate users towards text queries. Searching by text queries yields more effective and accurate results that
meet the needs of the users while at the same time preserves their familiarity with the
way traditional search engines operate. However, text-based image retrieval requires
images to be annotated i.e. they are related to text information. In recent years, much
effort has been invested on automatic image annotation methods, since the manual
assignment of keywords (which is necessary for text-based image retrieval) is a time
consuming and labour intensive procedure. This thesis focuses on image retrieval under the perspective of machine learning and covers di®erent aspects in this area. It
discusses and presents several studies referring to: (a) low-level feature extraction and
selection for the task of automatic annotation of images, (b) training algorithms that
can be utilized for keyword modeling based on visual content, and (c) the creation
of appropriate and reliable training data, to be used with the training scheme, using
the least manual effort. The main contribution is a new framework that can be used
to address the key issues in automatic keyword extraction by creating separate visual
models for all available keywords using the one-against-all paradigm to account for the
scalability and multiple keyword assignment problems. The prospective reader of this
thesis would be equipped with the ability to identify the key issues in automatic image
annotation and would be triggered to think ahead to propose alternative solutions.
Furthermore, this thesis can serve as a guide for researchers who want to experiment
with automatic keyword assignment to digital images.
meet the needs of the users while at the same time preserves their familiarity with the
way traditional search engines operate. However, text-based image retrieval requires
images to be annotated i.e. they are related to text information. In recent years, much
effort has been invested on automatic image annotation methods, since the manual
assignment of keywords (which is necessary for text-based image retrieval) is a time
consuming and labour intensive procedure. This thesis focuses on image retrieval under the perspective of machine learning and covers di®erent aspects in this area. It
discusses and presents several studies referring to: (a) low-level feature extraction and
selection for the task of automatic annotation of images, (b) training algorithms that
can be utilized for keyword modeling based on visual content, and (c) the creation
of appropriate and reliable training data, to be used with the training scheme, using
the least manual effort. The main contribution is a new framework that can be used
to address the key issues in automatic keyword extraction by creating separate visual
models for all available keywords using the one-against-all paradigm to account for the
scalability and multiple keyword assignment problems. The prospective reader of this
thesis would be equipped with the ability to identify the key issues in automatic image
annotation and would be triggered to think ahead to propose alternative solutions.
Furthermore, this thesis can serve as a guide for researchers who want to experiment
with automatic keyword assignment to digital images.
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