Active contour model for image segmentation based on Retinex correction and saliency

To achieve fast and accurate segmentation of natural images with intensity inhomogeneity and complicated backgrounds, an active contour model combined with Retinex correction and saliency analysis for image segmentation was proposed. Retinex correction was applied to obtain the reflection component of objects in images; this could suppress the influence of intensity inhomogeneity caused by nonuniform illumination. Moreover, the Retinex-corrected image reflected the image essence more objectively, ensuring the accuracy of subsequent salient information extraction and making it more practical and instructive. The introduction of saliency information into the active contour model was helpful for the effective segmentation of images with complex backgrounds. By combining Retinex correction and saliency information, a new active contour model energy equation was obtained, and the level set method was used to guide the curve evolution to achieve image segmentation. Through experimental analysis, the proposed method was proved to be fast, effective, and robust. The average processing time on the MSRA database is 4.277 s per image, and the average F value is 0.735. © 2019, Science Press. All right reserved.