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Thesis projects

Candidate Completion Date University Title (N) = Thesis in Norwegian
PhD degrees    
Christian Hartvedt 2011, UiB Interactivity in context-based image retrieval
Margrete Allern Brose 2010, UiTø Context-aware Image Retrieval
Lars-Jacob Hove 2009, UiB Visual Queries for Image Retrieval
Masters theses    
Roe Fyllingsnes 2008, UiB Mobile image retrieval
Jan-Erik Bråthen 2008, UiB Folksonomies
Siv Hansen 2008, UiB Multi Modal IR
Kawaljeet Singh Puri 2008, UiB A Global Image Retrieval System using the CIDOC CRM
Anne Staurland Aarbakke June 2007, UiTø M2S and CAIR: Image based information retrieval in mobile environments
Christian Hartvedt March 2007, UiB Utilizing context in ranking results from distributed image retrieval – the CAIRANK Prototyp
Silje Alfheim Dec.2006, UiTø Image Contexts and the Semantic Web
Kai Arne Bjørnenak Dec.2006, UiTø Images and Location data (N)
Arne Tøndersen June 2006, UiTø Image retrieval based on location and time
Kurt Jøran Nyland June 2006, UiTø Image Collections and Context (N)
Bachelor projects    
Per Thomas Bakken June 2007, UiTø Context detection on mobile units


Suggested master thesis projects

The table below presents some suggested master thesis projects based on the UiB project goals.

CAIM sub-project/thesis topics
CAIM Goal Area/
UiB project areas
UiB focus & perspective Thesis topic proposals
1 Dynamic context capture and management
1.a Image context model Definition of the concept of image context, context modeling including development of context descriptors for relationships among image objects 1. Evaluation/development of metadata models for capturing image context data in various application domains.
2. How can ontologies be used to make classification, information retrieval and collection updates more effective?
1.b Image description management Dynamic update of context descriptors from user feedback. Development of techniques for the construction and use of ‘folksonomies’ and ontologies. 1. How can user input (feedback) be utilized for object identification and inter-object relationship specifications?
- How do user defined 'folksonomies' relate to/effect application/domain ontologies?
- Will a constantly updated folksonomy for an image collection improve image retrieval?
- Can a game-based approach to identifying objects within an image improve identification of image components?
2 Multimodal information retrieval
2.a Context-based Image retrieval Focus on internal image contexts, e.g. relationships between image objects 1. Develop/extend algorithms for image retrieval using context data as defined in 1a and 1b

2.b Image retrieval using visual queries Development of a visual query language (VQL) for image retrieval 1. How can current VQLs be improved?
2.c Multi-modal information retrieval Text/audio retrieval from image input 1. Given visual input, how can image context data be utilized to retrieve related, multiple modal (text, audio) information?
2.d Multi-DB information retrieval Focus on retrieval from multiple image collections. 1. How can current information retrieval algorithms from multiple DB systems be adapted for a multiple media DB environment?
(related to 2c)
3 Result presentation
3.a Context-based ranking Focus on image component context 1. Develop and evaluate algorithms for "component-based" ranking (Related to 1b)

3.b Result ranking for image retrieval Focus on ranking/integration of image result sets from multiple image DBs 1. Develop/improve algorithms for merging/ranking result sets from multiple image DBs (re. 2d)
3.c Multi-modal result presentation Mixed media presentation. 1. Develop/improve current multi-modal result presentation
- potential case: VED prototypes; Flamenco http://flamenco.berkeley.edu/
- Are these algorithms domain dependent?

Modified: 06.09.2007 © University of Bergen - by: [an error occurred while processing this directive] Lars-Jacob Hove