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A framework for automated landmark recognition in community contributed image corpora

Hughes, Mark (2011) A framework for automated landmark recognition in community contributed image corpora. PhD thesis, Dublin City University.

Abstract
Any large library of information requires efficient ways to organise it and methods that allow people to access information efficiently and collections of digital images are no exception. Automatically creating high-level semantic tags based on image content is difficult, if not impossible to achieve accurately. In this thesis a framework is presented that allows for the automatic creation of rich and accurate tags for images with landmarks as the main object. This framework uses state of the art computer vision techniques fused with the wide range of contextual information that is available with community contributed imagery. Images are organised into clusters based on image content and spatial data associated with each image. Based on these clusters different types of classifiers are* trained to recognise landmarks contained within the images in each cluster. A novel hybrid approach is proposed combining these classifiers with an hierarchical matching approach to allow near real-time classification and captioning of images containing landmarks.
Metadata
Item Type:Thesis (PhD)
Date of Award:November 2011
Refereed:No
Supervisor(s):Jones, Gareth J.F.
Uncontrolled Keywords:information organisation; semantic tags; image retrieval
Subjects:Computer Science > Multimedia systems
Computer Science > Information retrieval
Computer Science > Image processing
DCU Faculties and Centres:Research Initiatives and Centres > Centre for Digital Video Processing (CDVP)
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 3.0 License. View License
Funders:FP6 STREP TriPOd
ID Code:16630
Deposited On:02 Dec 2011 11:39 by Noel Edward O'connor . Last Modified 20 Apr 2017 11:39
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