Unsupervised color coding for visualizing image classification results

Abstract

In this article, we describe a general purpose system that, given as input a segmented/classified image, automatically provides different visual outputs exploiting solid colors, color boundaries, and transparent colors. Moreover, if the names of the classes are given, the system automatically places a textual label in the less salient sub-region of the corresponding class. For color-class association and class label placement, we take into account the underlying image color and structure exploiting both saliency and superpixel representation. The color selection and the color-class association are formulated both as optimization problems and heuristically solved using a Local Search procedure. Results show the effectiveness of the proposed system on images having different content and different number of annotated regions.