A general image segmentation model and its application

This paper proposed a general image segmentation model, namely the energy-minimization based image segmentation (EMBIS) model. This model converts image segmentation into a controlled optimization process minimizing the weighted sum of the feature energy and spatial energy, which interpret the homogeneity restriction and spatial constraints, respectively. The EMBIS model provides a unified understanding of various existing segmentation algorithms, and can also serve as a framework for systematic generation of new segmentation algorithms. We provided four examples to illustrate that many existing segmentation algorithms are indeed specialized cases of this model with different instances of both energy functions. We also presented a case study to demonstrate how to use this model to create new algorithms and resulted in the spatial-constrained OTSU (SC-OTSU) algorithm, where segmentation can be achieved by minimizing the feature energy of the OTSU algorithm and spatial energy of the algorithm based on a simple MRF (SMRF) model. Evaluation on both synthetic and real images proved that novel segmentation algorithms derived form the proposed EMBIS model can provide accurate and efficient image segmentation.