Strong, Grant (2009) Similarity-based image organization and browsing. Masters thesis, Memorial University of Newfoundland.
- Accepted Version
Available under License - The author retains copyright ownership and moral rights in this thesis. Neither the thesis nor substantial extracts from it may be printed or otherwise reproduced without the author's permission.
Users do not always know what they want, in which case traditional query-based image retrieval approaches fail. This thesis serves to amend this shortcoming with a novel approach to organize and browse large image collections based on visual similarities in a way that extends the user's natural search sense. Starting with an unordered set of images, salient color and gradient information are extracted into feature vectors. A self organizing map (SOM) then projects these high-dimensional vectors onto a 2D canvas so that similar ones are grouped together. When browsing around on the canvas through intuitive operations like pan and zoom, a dynamic collage is generated that shows the most pertinent images. To make organizing larger image collections practical, a parallel SOM training algorithm is designed that runs on graphics processing units. The results of using a variety feature vectors are also evaluated.
|Item Type:||Thesis (Masters)|
|Additional Information:||Includes bibliographical references (leaves 55-56)|
|Department(s):||Science, Faculty of > Computer Science|
|Library of Congress Subject Heading:||Clumps (Information retrieval); Content-based image retrieval; Image processing; Self-organizing maps|
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