Robust Segmentation Based on Salient Region Detection Coupled Gaussian Mixture Model

The impressive progress on image segmentation has been witnessed recently. In this paper, an improved model introducing frequency-tuned salient region detection into Gaussian mixture model (GMM) is proposed, which is named FTGMM. Frequency-tuned salient region detection is added to achieve the saliency map of the original image, which is combined with the original image, and the value of the saliency map is added into the Gaussian mixture model in the form of spatial information weight. The proposed method (FTGMM) calculates the model parameters by the expectation maximization (EM) algorithm with low computational complexity. In the qualitative and quantitative analysis of the experiment, the subjective visual effect and the value of the evaluation index are found to be better than other methods. Therefore, the proposed method (FTGMM) is proven to have high precision and better robustness.

Automated summary

This paper proposes an improved image segmentation model, named FTGMM, which introduces frequency-tuned salient region detection into Gaussian mixture model. The results show that the proposed method has higher precision and better robustness compared to other methods.