  This paper considers the intra-image color-space of an object or a scene when
these are subject to a dominant single-source of variation. The source of
variation can be intrinsic or extrinsic (i.e., imaging conditions) to the
object. We observe that the quantized colors for such objects typically lie on
a planar subspace of RGB, and in some cases linear or polynomial curves on this
plane are effective in capturing these color variations. We also observe that
the inter-image color sub-spaces are robust as long as drastic illumination
change is not involved.
  We illustrate the use of this analysis for: discriminating between
shading-change and reflectance-change for patches, and object detection,
segmentation and recognition based on a single exemplar. We focus on images of
food items to illustrate the effectiveness of the proposed approach.
