In this article we describe the results of a study on similarity evaluation in image retrieval using color, object orientation, and relative position as content features, in a framework oriented to image repositories where the semantics of stored images are limited to a specific domain. The focus is not on a complete description of image content, which is supposed to be known to some extent, but on the extraction of simple and immediate features that can assure, through their combination, automated image analysis and efficient retrieval. Relevance feedback is introduced as an effective way to improve retrieval accuracy. A simple prototype system is also introduced that competes feature descriptors and allows users to enter queries, browse the retrieved images, and refine the results through relevance feedback analysis.
|Titolo:||Feature integration and relevance feedback analysis in image similarity evaluation|
|Data di pubblicazione:||1998|
|Digital Object Identifier (DOI):||10.1117/1.482646|
|Appare nelle tipologie:||1.1 Articolo in rivista|