Image Segmentation Algorithm Combining Visual Salient Regions and Active Contour

Abstract

When the traditional regional active contour model is used to segment the weak edge image, the evolution curve is subject to background interference, and it is easy to fall into the local extreme value, which leads to slow evolution speed. Moreover, as the local term only considers the spatial information, it cannot better retain the target boundary, which affects the segmentation accuracy. To solve the above problems, firstly, this paper uses the improved saliency detection algorithm to preprocess the original image, obtains the target candidate regions and automatically sets the initial contour curve. In addition, the obtained priori information of the target is combined with the bitmap with the maximum contrast in the image to be segmented. An adaptive symbolic function is designed to weight the optimized LoG (Laplacian of Gaussian) energy terms, in a linear fashion into RSF (region-scalable fitting) model, improving the adaptive ability of the model. Secondly, a new local grayscale measure is proposed, which is combined with local kernel function to improve the local energy term. It can improve the sensitivity of the model at the weak edge, and accurately locate the target boundary. Experimental results show that this model can automatically set the initial contour and effectively retain the target edge details. Visual and quantitative experimental results show that this model is superior to some mainstream active contour models.

Keywords

• |visual saliency detection|adaptive symbolic function|local gray measure|active contour model|image segmentation