Generative probabilistic models for image retrieval

Stathopoulos, Vassilios (2012) Generative probabilistic models for image retrieval. PhD thesis, University of Glasgow.

Abstract

Searching for information is a recurring problem that almost everyone has faced at some point. Being in a library looking for a book, searching through newspapers and magazines for an old article or searching through emails for an old conversation with a colleague are some examples of the searching activity. These are some of the many situations where someone; the “user”; has some vague idea of the information he is looking for; an “information need”; and is searching through a large number of documents, emails or articles; “information items”; to find the most “relevant” item for his purpose.

In this thesis we study the problem of retrieving images from large image archives. We consider two different approaches for image retrieval. The first approach is content based image retrieval where the user is searching images using a query image. The second approach is semantic retrieval where the users expresses his query using keywords. We proposed a unified framework to treat both approaches using generative probabilistic models in order to rank and classify images with respect to user queries. The methodology presented in this Thesis is evaluated on a real image collection and compared against state of the art methods.

• Item Type: Thesis (PhD)
• Qualification Level: Doctoral
• Keywords: Information Retrieval, Image Retrieval, Probabilistic Models, Mixture Models, Bayesian Inference
• Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science; Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science; Q Science > QA Mathematics
• Colleges/Schools: College of Science and Engineering > School of Computing Science
• Supervisor's Name: Jose, Prof. Joemon M.
• Date of Award: 2012
• Depositing User: Vassilios Stathopoulos
• Unique ID: glathesis:2012-3360
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 03 May 2012
• Last Modified: 10 Dec 2012 14:06
• URI: https://theses.gla.ac.uk/id/eprint/3360