In this paper, different ways of aggregating color information in edge extraction task are explored. One of these approaches is based on a previous transformation of the RGB images into a new color space of 8 dimensions. This increases the number of dimensions, making differences between colors easier to detect, and in this way improving the performance of edge detection task. Sobel and Canny algorithms are employed over the RGB images and its transformed version of 8 dimensions -that we have named Super8 image-. The set of employed images is the one from Berkeley's dataset. In order to evaluate the performance, precision, recall and F measure are computed. The way of aggregating the color information is showed to be relevant. For some well-known algorithms, the new 8 dimension color space overtakes RGB's for edge detection problem.