Applying Aggregation Concepts for Image Search

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Applying Aggregation Concepts for Image Search
Through the influx of information content on the Internet, a number of image search methodologies have been presented and implemented to increase the accuracy of image retrieval including keywords, object classification and feature processing. Both keyword and object classification models rely heavily on human subjects, which is time-consuming and error-prone with inconsistency in word agreement. We propose two feature processing methods without human intervention. The feature collage algorithm compares images based on particular features such as color histogram whereas the feature independent algorithm considers each feature's dimension as independent contributors to the image quality. Using query-by-example, we organize images using rank aggregation methods, previously applied in text information retrieval. We show through empirical experimentation the benefits of our feature processing algorithms over traditional CBIR approaches.
Brandeis Marshall, Dale-Marie Wilson
Added 12 Dec 2010
Updated 12 Dec 2010
Type Journal
Year 2008
Where ISM
Authors Brandeis Marshall, Dale-Marie Wilson
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