Representation of colored images by manifolds embedded in higher dimensional non-Euclidean space

In image analysis, processing and understanding, it is highly desirable to process the image and feature domains by methods that are specific to these domains. We show how the geometrical framework for scale-space flows is most convenient for this purpose, and demonstrate, as an example, how one can switch continuously between different processing flows of images and color domains. The parameter that interpolates between the norms is the luminance strength, taken here as a local function of the image embedding space. The resulting spatial and/or luminance preserving flow can be used for conditional denoising, enhancement and segmentation. This example demonstrates that the proposed framework can incorporate context or task dependent data, furnished by either the human user or by an active vision subsystem, in a coherent and convenient way.