Roadside vegetation segmentation with Adaptive Texton Clustering Model

Abstract

Automatic roadside vegetation segmentation is important for various real-world applications and one main challenge is to design algorithms that are capable of representing discriminative characteristics of vegetation while maintaining robustness against environmental effects. This paper presents an Adaptive Texton Clustering Model (ATCM) that combines pixel-level supervised prediction and cluster-level unsupervised texton occurrence frequencies into superpixel-level majority voting for adaptive roadside vegetation segmentation. The ATCM learns generic characteristics of vegetation from training data using class-specific neural networks with color and texture features, and adaptively incorporates local properties of vegetation in every test image using texton based adaptive K-means clustering. The adaptive clustering groups test pixels into local clusters, accumulates texton frequencies in every cluster and calculates cluster-level class probabilities. The pixel- and cluster-level probabilities are integrated via superpixel-level voting to determine the category of every superpixel. We evaluate the ATCM on three real-world datasets, including the Queensland Department of Transport and Main Roads, the Croatia, and the Stanford background datasets, showing very competitive performance to state-of-the-art approaches.