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Content-based image retrieval: color and edges
Robert S. Gray
Dartmouth PCS-TR95-252

Abstract: One of the tools that will be essential for future electronic publishing is a powerful image retrieval system. The author should be able to search an image database for images that convey the desired information or mood; a reader should be able to search a corpus of published work for images that are relevant to his or her needs. Most commercial image retrieval systems associate keywords or text with each image and require the user to enter a keyword or textual description of the desired image. This text-based approach has numerous drawbacks -- associating keywords or text with each image is a tedious task; some image features may not be mentioned in the textual description; some features are ``nearly impossible to describe with text''; and some features can be described in widely different ways [Niblack, 1993]. In an effort to overcome these problems and improve retrieval performance, researchers have focused more and more on content-based image retrieval in which retrieval is accomplished by comparing image features directly rather than textual descriptions of the image features. Features that are commonly used in content-based retrieval include color, shape, texture and edges. In this report we describe a simple content-based system that retrieves color images on the basis of their color distributions and edge characteristics. The system uses two retrieval techniques that have been described in the literature -- i.e. histogram intersection to compare color distributions and sketch comparison to compare edge characteristics. The performance of the system is evaluated and various extensions to the existing techniques are proposed.


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