Complex Color Space Segmentation to Classify Objects in Urban Environments

Color image segmentation divides the image into areas that represent different objects and focus points. One of the biggest problems in color image segmentation is the lack of homogeneity in the color of real urban images, which generates areas of over-segmentation when traditional color segmentation techniques are used. This article describes an approach to detecting and classifying objects in urban environments based on a new chromatic segmentation to locate focus points. Based on components a and b on the CIELab space, we define a chromatic map on the complex space to determine the highest threshold values by comparing neighboring blocks and thus divide various areas of the image automatically. Even though thresholds can result in broad segmentation areas, they suffice to locate centroids of patches on the color image that are then classified using a convolutional neural network (CNN). Thus, this broadly segmented image helps to crop only outlying areas instead of classifying the entire image. The CNN is trained to use six classes based on the patches drawn from the database of reference images from urban environments. Experimental results show a high score for classification accuracy that confirms the contribution of this segmentation approach.