Similarity-based image organization and browsing

Strong, Grant (2009) Similarity-based image organization and browsing. Masters thesis, Memorial University of Newfoundland.

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Abstract

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)
URI: http://research.library.mun.ca/id/eprint/8930
Item ID: 8930
Additional Information: Includes bibliographical references (leaves 55-56)
Department(s): Science, Faculty of > Computer Science
Date: 2009
Date Type: Submission
Library of Congress Subject Heading: Clumps (Information retrieval); Content-based image retrieval; Image processing; Self-organizing maps

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