Improving Graph-Based Image Segmentation Using Nonlinear Color Similarity Metrics

Abstract

We present a new segmentation method called weighted Felzenszwalb and Huttenlocher (WFH), an improved version of the well-known graph-based segmentation method, Felzenszwalb and Huttenlocher (FH). Our algorithm uses a nonlinear discrimination function based on polynomial Mahalanobis Distance (PMD) as the color similarity metric. Two empirical validation experiments were performed using as a golden standard ground truths (GTs) from a publicly available source, the Berkeley dataset, and an objective segmentation quality measure, the Rand dissimilarity index. In the first experiment the results were compared against the original FH method. In the second, WFH was compared against several well-known segmentation methods. In both cases, WFH presented significant better similarity results when compared with the golden standard and segmentation results presented a reduction of over-segmented regions.