A New Pixel-Oriented Visualization Technique Through Color Image

Frédéric Blanchard, Laurent Lucas et Michel Herbin

Color image is often considered as a fundamental perceptual unit of visualization. In this paper, we suggest using this medium (color image) to summarize multidimensional data and thus to turn a data set into a meaningful insight. The methodology we use is based on the theory of Keim for designing pixel-oriented visualization techniques. The technique we propose consists in a three-step pipeline. The first one is devoted to dimensionality reduction by projecting multidimensional data into a three-dimensional space. In this work, we use the classical principal component analysis (PCA) to reduce the dimension to three. The second step, called color mapping, is based on the reverse color information transformation defined by Ohta et al. This stage is the main novelty of this work in addition to the pipeline itself. The third step consists in a pixel-oriented method to display large data sets with an image using space-filling curve techniques. The combination of these steps (first, dimensionality reduction with PCA, second, color mapping with color information of Ohta et al., and third, space-filling curve with Peano-Hilbert curve) allows us to obtain a new unsupervised visualization technique through color images. This blind (i.e. unsupervised) technique using a color image gives a previsualization that can be used before exploring the data set or choosing more effective colors. Some applications are proposed in the field of multicomponent image visualization.