Abstract
Unsupervised image segmentation is of essential relevance for many computer vision applications and remains a difficult task despite of decades of intensive research. In particular, the parameter selection problem has not received the due attention in the past. Researchers typically claim to have empirically fixed the parameter values or train in advance based on manual ground truth. These approaches are not optimal and lack an adaptive behavior in dealing with a particular image. In this work we adopt the ensemble combination principle to solve the parameter selection problem in image segmentation. It explores the parameter space without the need of ground truth. The experimental results including a comparison with ground truth based training demonstrate the effectiveness of our framework.
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