Improved watershed algorithm for color image segmentation

To overcome over-segmentation of Watershed transform, a novel improved Watershed algorithm based on adaptive marker-extraction is proposed. The original marker-based Watershed algorithm is improved by considering multiple feature information of local minima and adaptively selecting threshold. The proposed method consists of five steps: 1) Calculating gradient directly with color vectors; 2) Low-pass filtering of gradient image with BTPF; 3) Employing Hminima transform to extract true local minima whose depth is lower than that of threshold H, which is adaptively adjusted according to gradient image's statistical character. 4) Further marker-extraction being based on water basin scale. 5) Imposing the markers on the original gradient image as its minima; finally, Watershed transform is implied to the marked gradient image to segment the image. Experimental results show that, compared with other testing Watershed algorithms, the proposed method can more efficiently reduce over-segmentation and obtain better segmentation performance with lower computational complexity; in addition, it has better anti-noise performance and edge-location capability as well.