Saliency-guided level set model for automatic object segmentation

Highlights：

• A global saliency-guided energy term is defined using the saliency map to roughly extract the objects, which significantly improves the segmentation efficiency and the robustness to noise and to initialize the SLSM.

• Unlike most existing level set models only using grayscale information of color images, the proposed local multichannel-based energy term using the CIEL*a*b* color space successfully achieves color image segmentation.

• A novel graph cuts based method is proposed using the Heaviside function of the level set model to define the data term that can avoid the occurrence of small isolated region in the final segmentation.

• A new automatic initialization method using graph cuts to segment the image saliency map avoids the tedious and time-consuming manual initialization and further improves the segmentation efficiency.