  Vegetation segmentation from roadside data is a field that has received
relatively little attention in present studies, but can be of great potentials
in a wide range of real-world applications, such as road safety assessment and
vegetation condition monitoring. In this paper, we present a novel approach
that generates class-semantic color-texture textons and aggregates superpixel
based texton occurrences for vegetation segmentation in natural roadside
images. Pixel-level class-semantic textons are first learnt by generating two
individual sets of bag-of-word visual dictionaries from color and filter-bank
texture features separately for each object class using manually cropped
training data. For a testing image, it is first oversegmented into a set of
homogeneous superpixels. The color and texture features of all pixels in each
superpixel are extracted and further mapped to one of the learnt textons using
the nearest distance metric, resulting in a color and a texture texton
occurrence matrix. The color and texture texton occurrences are aggregated
using a linear mixing method over each superpixel and the segmentation is
finally achieved using a simple yet effective majority voting strategy.
Evaluations on two public image datasets from videos collected by the
Department of Transport and Main Roads (DTMR), Queensland, Australia, and a
public roadside grass dataset show high accuracy of the proposed approach. We
also demonstrate the effectiveness of the approach for vegetation segmentation
in real-world scenarios.
