Authors:
Iuri Frosio
1
and
Ed R. Ratner
2
Affiliations:
1
NVIDIA, United States
;
2
Lyrical Labs, United States
Keyword(s):
Superpixel, Adaptive Segmentation, Machine Learning, Segmentation Quality Metric.
Related
Ontology
Subjects/Areas/Topics:
Color and Texture Analyses
;
Computer Vision, Visualization and Computer Graphics
;
Image and Video Analysis
;
Segmentation and Grouping
;
Visual Attention and Image Saliency
Abstract:
We introduce here a model for the evaluation of the segmentation quality of a color image. The model parameters were learned from a set of examples. To this aim, we first segmented a set of images using a traditional graph-cut algorithm, for different values of the scale parameter. A human observer classified these images into three classes: under-, well- and over-segmented. This classification was employed to learn the parameters of the segmentation quality model. This was used to automatically optimize the scale parameter of the graph-cut segmentation algorithm, even at a local scale. Experimental results show an improved segmentation quality for the adaptive algorithm based on our segmentation quality model, which can be easily applied to a wide class of segmentation algorithms.