Mosaic-Based Relevance Feedback for Image Retrieval

Mosaic-Based Relevance Feedback for Image Retrieval

Odej Kao (University of Paderborn, Germany) and Ingo la Tendresse (Technical University of Clausthal, Germany)
Copyright: © 2005 |Pages: 5
DOI: 10.4018/978-1-59140-557-3.ch159
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Abstract

A standard approach for content-based image retrieval (CBIR) is based on the extraction and comparison of features usually related to dominant colours, shapes, textures and layout (Del Bimbo, 1999). These features are a-priori defined and extracted, when the image is inserted into the database. At query time the user submits a similar sample image (query-by-sample-image) or draws a sketch (query-by-sketch) of the sought archived image. The similarity degree of the current query image and the target images is determined by calculation of a multidimensional distance between the corresponding features. The computed similarity values allow the creation of an image ranking, where the first k, usually k=32 or k=64, images are considered retrieval hits. These are chained in a list called ranking and then presented to the user. Each of these images can be used as a starting point for a refined search in order to improve the obtained results.

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