Enhancing spatio-chromatic representation with more-than-three color coding for image description

Ivet Rafegas, Javier Vazquez-Corral, Robert Benavente, Maria Vanrell, Susana Alvarez

Abstract:

Extraction of spatio-chromatic features from color images is usually performed independently on each
color channel. Usual 3D color spaces, such as RGB, present a high inter-channel correlation for natural
images. This correlation can be reduced using color-opponent representations, but the spatial structure
of regions with small color differences is not fully captured in two generic Red-Green and Blue-Yellow
channels. To overcome these problems, we propose a new color coding that is adapted to the specific content of each image. Our proposal is based on two steps: (a) setting the number of channels to the number
of distinctive colors we find in each image (avoiding the problem of channel correlation), and (b) building a channel representation that maximizes contrast differences within each color channel (avoiding the
problem of low local contrast). We call this approach more-than-three color coding (MTT) to enhance the
fact that the number of channels is adapted to the image content. The higher color complexity an image
has, the more channels can be used to represent it. Here we select distinctive colors as the most predominant in the image, which we call color pivots, and we build the new color coding using these color pivots
as a basis. To evaluate the proposed approach we measure its efficiency in an image categorization task.
We show how a generic descriptor improves its performance at the description level when applied on the
MTT coding