Color histograms are widely used for content-based image retrieval. Their advantages are efficiency, and insensitivity to small changes in camera viewpoint. However, a histogram is a coarse characterization of an image, and so images with very different appearances can have similar histograms. This is particularly important for large image databases, in which many images can have similar color histograms. We will show how to find a relationship between histograms and elliptic curves, in order to define a similarity color feature based onto parametric elliptic equations. This equations are directly involved in the Fermat's Last Theorem, thus representing a solution which is interesting in terms of theory and parametric properties.
Fermat theorem and elliptic color histogram features
Malizia A;
2003-01-01
Abstract
Color histograms are widely used for content-based image retrieval. Their advantages are efficiency, and insensitivity to small changes in camera viewpoint. However, a histogram is a coarse characterization of an image, and so images with very different appearances can have similar histograms. This is particularly important for large image databases, in which many images can have similar color histograms. We will show how to find a relationship between histograms and elliptic curves, in order to define a similarity color feature based onto parametric elliptic equations. This equations are directly involved in the Fermat's Last Theorem, thus representing a solution which is interesting in terms of theory and parametric properties.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
