  Unsupervised evaluation of segmentation quality is a crucial step in image
segmentation applications. Previous unsupervised evaluation methods usually
lacked the adaptability to multi-scale segmentation. A scale-constrained
evaluation method that evaluates segmentation quality according to the
specified target scale is proposed in this paper. First, regional saliency and
merging cost are employed to describe intra-region homogeneity and inter-region
heterogeneity, respectively. Subsequently, both of them are standardized into
equivalent spectral distances of a predefined region. Finally, by analyzing the
relationship between image characteristics and segmentation quality, we
establish the evaluation model. Experimental results show that the proposed
method outperforms four commonly used unsupervised methods in multi-scale
evaluation tasks.
